FLOPs to train GPT-3: 답변 267개

“Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.”

보관된 답변, 최신순, 2025년 1월부터 2026년 10월까지. 각 답변은 기록된 프롬프트와 함께 보관됩니다.

1. Step 5 Preview StepFun, 2026년 10월 9일
   Using the remembered GPT-3 specs: - Parameters: \(N \approx 175 \times 10^9 = 175\text{B}\) - Training tokens: \(D \approx 300 \times 10^9 = 300\text{…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

2. Claude Haiku 5.5 Anthropic, 2026년 10월 9일
   I'll estimate this from known architectural facts and the standard scaling-law approximation, without looking anything up. Step 1: Recall GPT-3's size…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

3. Mistral Large 4 Mistral, 2026년 10월 6일
   I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Par…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

4. Ling 3.1 Flash inclusionAI, 2026년 10월 6일
   To estimate the total FLOPs (floating-point operations) required to train GPT-3, I'll use the known architectural and training parameters of GPT-3 and…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

5. GPT-6.1 Sol OpenAI, 2026년 9월 30일
   Estimated training compute for the largest GPT-3 model: approximately \(3.2 \times 10^{23}\) FLOPs. This is a back-of-the-envelope estimate, without a…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

6. Ternary Bonsai 2 27B Prism, 2026년 9월 28일
   Estimating FLOPs to Train GPT-3 Key Parameters (from the GPT-3 paper, Brown et al. 2020) Quantity Value ------ Parameters (N) ≈ 175 billion (1.75 × 10…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

7. Solar Pro 4 Upstage, 2026년 9월 28일
   To estimate the total FLOPs required to train GPT-3, I'll focus on the largest and most widely referenced version: GPT-3 175B (175 billion parameters)…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

8. Solar Mini 4 Upstage, 2026년 9월 28일
   Estimating GPT-3 Training FLOPs To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I …
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

9. Qwen3.8 Omni Flash Qwen, 2026년 9월 28일
   Assuming you mean the full 175B-parameter GPT-3 model’s main training run, a good estimate is: \[ \boxed{\text{about }3\times 10^{23}\text{ FLOPs}} \]…
   기록된 프롬프트

   Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

10. Qwen3.8 Max Prime Qwen, 2026년 9월 28일
    Estimating FLOPs to Train GPT-3 Key Facts About GPT-3 Property Value ----------------- Parameters (N) 175 billion Training tokens (D) 300 billion Arch…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

11. Qwen3.8 Max (0902) Qwen, 2026년 9월 28일
    Estimating the FLOPs to Train GPT-3 Key Facts About GPT-3 Property Value ----------------- Parameters (N) 175 billion Training tokens (D) 300 billion …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

12. Qwen3.8 Flash Qwen, 2026년 9월 28일
    A reasonable estimate is: \[ \boxed{\text{about } 3 \times 10^{23}\ \text{FLOPs}} \] This is the commonly cited order of magnitude for GPT-3 training …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

13. Qwen3.7 Flash Qwen, 2026년 9월 28일
    Here’s a step-by-step reasoning to estimate the total FLOPs required to train GPT-3, relying only on architectural principles, standard deep learning …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

14. Nemotron 3 Nano Omni NVIDIA, 2026년 9월 28일
    Step‑by‑step estimate of the total number of floating‑point operations (FLOPs) needed to train GPT‑3 Item What we need to know Reasonable value (deriv…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

15. Muse Glimmer 30B Meta, 2026년 9월 28일
    GPT-3 is a dense Transformer decoder with 175B parameters trained on 300B tokens of text. The standard back-of-the-envelope for training FLOPs of a de…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

16. MiMo-V2.6-Pro Xiaomi, 2026년 9월 28일
    Estimating Training FLOPs for GPT-3 (175B) Key Formula The standard estimate for total training compute is: $$\text{FLOPs} \approx 6 \times N \times D…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

17. MiMo-V2.6-Flash Xiaomi, 2026년 9월 28일
    Estimating GPT-3 Training FLOPs Known Facts About GPT-3 - Parameters (N): 175 billion (175 × 10⁹) - Training tokens (D): 300 billion (300 × 10⁹) - Arc…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

18. Mercury 2.5 Inception, 2026년 9월 28일
    To estimate the total floating-point operations (FLOPs) required to train GPT-3, I will break down the known architecture and training dataset size, t…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

19. Ling 3.0 Flash Sante inclusionAI, 2026년 9월 28일
    Here's my step-by-step estimation of GPT-3's training compute: Step 1: Identify the model scale GPT-3 has 175 billion parameters (1.75 × 10¹¹). Step 2…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

20. Ling 3.0 Flash Fin inclusionAI, 2026년 9월 퇴역, 2026년 9월 28일
    Estimating the Total FLOPs to Train GPT-3 Key Parameters of GPT-3 (from Brown et al., 2020) Parameter Value ------ Number of parameters (P) 175 billio…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

21. Ling 3.0 Flash inclusionAI, 2026년 9월 28일
    Estimating GPT-3 Training FLOPs from First Principles Key Known Facts About GPT-3 (from memory/prior knowledge) Parameter Value ------ Number of param…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

22. Laguna XS 2.1 Poolside, 2026년 9월 28일
    To estimate the total number of FLOPs required to train GPT-3, we use a standard formula for large language models: Total FLOPs ≈ 6 × Parameters × Tok…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

23. Laguna S 2.1 Poolside, 2026년 9월 28일
    To estimate the total number of FLOPs required to train GPT-3, we use the commonly accepted formula for training a transformer model, which is approxi…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

24. Hy3 Tencent, 2026년 9월 28일
    Estimate: Roughly \(3 \times 10^{23}\) floating‑point operations (about 300 sextillion FLOPs) to train the 175 B‑parameter GPT‑3. --- Step‑by‑step rea…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

25. Grok 4.7 xAI, 2026년 9월 28일
    About \(3 \times 10^{23}\) FLOPs (roughly \(3.1 \times 10^{23}\)) for the 175B GPT-3 model. Reasoning 1. Parameter count. The full GPT-3 model has \(N…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

26. GPT-6 Sol Pro OpenAI, 2026년 9월 28일
    A standard estimate is about \(3 \times 10^{23}\) FLOPs to train GPT-3. 1. GPT-3 has approximately 175 billion parameters: \(N = 1.75 \times 10^{11}\)…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

27. GPT-6 Sol OpenAI, 2026년 9월 28일
    A reasonable estimate is \(3\times10^{23}\) FLOPs to train the 175-billion-parameter GPT-3 model. 1. Assume the training scale: GPT-3 has about \(N=17…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

28. GPT-6 Luna Pro OpenAI, 2026년 9월 28일
    A standard estimate for training compute is \[ \text{FLOPs} \approx 6ND, \] where \(N\) is the number of model parameters and \(D\) is the number of t…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

29. GPT-6 Luna OpenAI, 2026년 9월 28일
    A standard estimate for training a dense language model is \[ \text{training FLOPs} \approx 6ND, \] where \(N\) is the number of model parameters and …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

30. GLM 5.3 Prime Zhipu, 2026년 9월 28일
    Estimating Training FLOPs for GPT-3 Step 1: Recall the key parameters From the GPT-3 paper (from memory): - Parameters (N): 175 billion - Training tok…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

31. GLM 5.3 FlashX Zhipu, 2026년 9월 28일
    Estimating GPT-3 Training FLOPs Step 1: Recall Key Model Facts From the GPT-3 paper (which I know from training data): - Parameters: 175 billion (175 …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

32. GLM 5.3 Flash Zhipu, 2026년 9월 28일
    Estimating GPT-3 Training FLOPs Step 1: Model Size GPT-3's largest version has 175 billion parameters (1.75 × 10¹¹). Step 2: Training Tokens The train…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

33. GLM 5.3 Zhipu, 2026년 9월 28일
    Estimating Training FLOPs for GPT-3 Step 1: Recall the key formula For transformer language models, a well-known rule of thumb (from the Chinchilla/Ka…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

34. DeepSeek V4.1 Flash DeepSeek, 2026년 9월 28일
    To estimate GPT-3 training FLOPs from memory, use the standard dense-transformer training compute approximation: \[ C \approx 6ND \] where: - \(N\) = …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

35. DeepSeek V4 Flash Vision Exp DeepSeek, 2026년 9월 28일
    To estimate the training FLOPs for GPT-3, I’ll use three key numbers and a standard rule of thumb for transformer training. Step 1: Model size GPT-3 i…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

36. Command A+ Cohere, 2026년 9월 28일
    To estimate the total FLOPs required to train GPT-3, I need to analyze the model's architecture, training process, and make reasonable assumptions bas…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

37. Claude Sonnet 5.5 Anthropic, 2026년 9월 28일
    Estimate: about 3 × 10²³ FLOPs Step 1: Recall the model size and training data. - GPT-3 (175B version) has N ≈ 175 × 10⁹ parameters. - It was trained …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

38. Claude Opus 5.5 Anthropic, 2026년 9월 28일
    Estimating the Training FLOPs for GPT-3 Step 1: Recall the key facts about GPT-3 - Parameters (N): about 175 billion (1.75 × 10¹¹) - Training tokens (…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

39. GPT-6 Astra OpenAI, 2026년 9월 4일
    For the 175-billion-parameter GPT-3 model, I estimate approximately \(3.2 \times 10^{23}\) FLOPs for its main training run, using remembered model spe…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

40. Muse Spark 1.3 Contributor Meta, 2026년 9월 2일
    This is a classic Fermi estimate for a Transformer. You can get within a factor of 2 with just 3 numbers: Training FLOPs $\approx$ FLOPs per token $\t…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

41. Muse Spark 1.3 Meta, 2026년 9월 2일
    The famous estimate for Transformer training is: Compute 6 x Params x Tokens For GPT-3 that comes out to 3e23 FLOPs. Here's how to get it from scratch…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

42. Mercury 2.5 Preview Inception, 2026년 9월 퇴역, 2026년 9월 2일
    To estimate the total FLOPs required to train GPT-3, we can break down the calculation using known architectural and training statistics from the mode…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

43. Hy4 Preview Tencent, 2026년 9월 2일
    Here is the step-by-step estimation for the total number of Floating Point Operations (FLOPs) required to train GPT-3. The Core Formula For a standard…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

44. Granite 4.2 8B IBM, 2026년 9월 2일
    To estimate the total FLOPs for training GPT-3 without external data, follow these steps: 1. Identify key parameters: - Model size: 175B parameters (G…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

45. Gemini 3.8 Flash Google, 2026년 9월 2일
    To estimate the total floating-point operations (FLOPs) required to train the full GPT-3 model, we can use the standard theoretical scaling law for de…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

46. Ox Alpha Code names, 2026년 8월 퇴역, 2026년 8월 24일
    Estimating GPT-3 Training FLOPs Step 1: Model Size GPT-3 has 175 billion parameters (the largest variant). Step 2: FLOPs per Token per Parameter For a…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

47. Seed 2.1 Turbo ByteDance, 2026년 8월 18일
    To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we break down the problem step by step, focusing on the lar…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

48. Seed 2.0 Code ByteDance, 2026년 8월 18일
    Step-by-Step Estimate of GPT-3 Training FLOPs (All reasoning uses no external search—relies on transformer first principles, GPT-3 specs (175B paramet…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

49. Qwen3.8 27B Qwen, 2026년 8월 18일
    A good estimate uses the standard LLM training compute rule of thumb: \[ \text{Training FLOPs} \approx 6ND \] where: - \(N\) = number of model paramet…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

50. Qwen3.8 2.4T A95B Qwen, 2026년 8월 18일
    A reasonable estimate is: \[ \boxed{\sim 3 \times 10^{23} \text{ FLOPs}} \] More specifically, about: \[ \boxed{3.1 \times 10^{23} \text{ FLOPs}} \] f…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

51. Nemotron 3.5 Lightning NVIDIA, 2026년 8월 18일
    To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 without searching the internet, we must rely on the standard…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

52. LFM2.5-2.6B Liquid, 2026년 8월 18일
    To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to break down the calculation into the model's arch…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

53. Grok 4.6 xAI, 2026년 8월 18일
    The standard approximation for the training compute of a dense transformer is \(C \approx 6ND\) FLOPs, where \(N\) is the number of parameters and \(D…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

54. Gemini 3.7 Flash Google, 2026년 8월 18일
    To estimate the total floating-point operations (FLOPs) required to train GPT-3, we can use the standard scaling laws and computational approximations…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

55. Dots3-Note Preview dots, 2026년 8월 18일
    To estimate the total number of FLOPs required to train GPT-3, I rely on the model's known specifications and a standard rule of thumb for transformer…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

56. DeepSeek V4 Pro 0813 DeepSeek, 2026년 8월 18일
    From memory, GPT-3 has about 175 billion parameters and was trained on about 300 billion tokens. A widely used approximation for dense transformer tra…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

57. DeepSeek V4 Flash 0731 DeepSeek, 2026년 8월 5일
    The total training FLOPs for GPT-3 is approximately: 3 × 10²³ FLOPs or about 300 zettaFLOPs (more specifically 315 zettaFLOPs). Step-by-step reasoning…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

58. Claude Opus 5 Anthropic, 2026년 7월 24일
    Step 1: Recall the two key numbers about GPT-3 - Parameters: 175 billion (1.75 × 10¹¹) - Training tokens: 300 billion (3 × 10¹¹) These are the two qua…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

59. Gemini 3.6 Flash Google, 2026년 7월 23일
    To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use a standard rule-of-thumb formula derived from Tr…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

60. Inkling Thinking Machines, 2026년 7월 21일
    Here is the step-by-step reasoning to estimate the training FLOPs for GPT-3 (the 175B parameter version). Step 1: Identify the model size (N) GPT-3’s …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

61. Muse Spark 1.1 Meta, 2026년 7월 16일
    For GPT-3 175B, the standard estimate is: 3 x 10^23 FLOPs If you count a multiply-add as 1 FLOP, 1.5e23. That's 3,600 petaflop-days. Here's how you ge…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

62. Kimi K3 Moonshot, 2026년 7월 16일
    Assume “GPT-3” refers to the flagship 175-billion-parameter model. 1. Use the standard transformer training-cost approximation For a dense transformer…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

63. Grok 4.5 xAI, 2026년 7월 9일
    Setup and known quantities. GPT-3 (the largest model in the family) has \(N \approx 175 \times 10^9 = 1.75 \times 10^{11}\) trainable parameters. It w…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

64. GPT-5.6 Terra OpenAI, 2026년 7월 9일
    A good back-of-the-envelope estimate is: \[ \boxed{\text{Training FLOPs for GPT-3} \approx 3\times10^{23}\text{ FLOPs}} \] or roughly 300 zettaFLOPs t…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

65. GPT-5.6 Sol OpenAI, 2026년 7월 9일
    A standard back-of-the-envelope estimate for dense Transformer training is: \[ C \approx 6ND \] where: - \(N\) = number of trainable parameters - \(D\…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

66. GPT-5.6 Luna Pro OpenAI, 2026년 7월 9일
    A reasonable estimate is: \[ \boxed{\text{about }3\times 10^{23}\text{ FLOPs}} \] Here is the reasoning. 1. Model size GPT-3 has approximately \[ N = …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

67. GPT-5.6 Luna OpenAI, 2026년 7월 9일
    A standard back-of-the-envelope estimate for training a dense Transformer is \[ \text{Training FLOPs} \approx 6ND, \] where: - \(N\) = number of model…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

68. Claude Sonnet 5 Anthropic, 2026년 6월 30일
    Estimating FLOPs to Train GPT-3 I'll work through this using the well-known approximation method, building up from first principles rather than recall…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

69. North Mini Code Cohere, 2026년 6월 24일
    Answer (in words): Training GPT‑3 required on the order of 10²³–10²⁴ floating‑point operations (FLOPs) – roughly 3 × 10²³ FLOPs if we use the numbers …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

70. GLM 5.2 Zhipu, 2026년 6월 16일
    Based on standard machine learning scaling laws and the known architecture of GPT-3, the estimated total number of FLOPs (Floating Point Operations) r…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

71. OpenRouter Fusion · Quality (Jun 2026) OpenRouter, 2026년 6월 13일
    I'll work through this estimation for you. Estimating GPT-3's Training FLOPs Step 1: Use the standard transformer compute formula For training a dense…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

72. OpenRouter Fusion · Budget (Jun 2026) OpenRouter, 2026년 6월 13일
    To estimate the total computational effort required to train the largest version of GPT-3 (GPT-3 175B), we can use established scaling laws and heuris…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

73. Kimi K2.7 Code Moonshot, 2026년 6월 13일
    Here is a rough, back-of-the-envelope estimate based on the standard scaling-law formula for training decoder-only transformers. 1. The key numbers fo…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

74. Claude Fable 5 Anthropic, 2026년 6월 9일
    Estimating GPT-3 Training FLOPs Step 1: Recall the key facts about GPT-3 - Parameters (N): 175 billion (1.75 × 10¹¹) - Training tokens (D): 300 billio…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

75. Nemotron 3.5 Content Safety NVIDIA, 2026년 6월 6일
    User Safety: safe
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

76. Nemotron 3 Ultra NVIDIA, 2026년 6월 6일
    Based on the details published in the original GPT-3 paper ("Language Models are Few-Shot Learners", Brown et al., 2020) and standard Transformer trai…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

77. Qwen3.7 Plus Qwen, 2026년 6월 4일
    To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, we can rely on well-established ru…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

78. MiniMax M3 MiniMax, 2026년 6월 2일
    Estimating GPT-3 Training FLOPs Step 1: Identify the Key Parameters From what's publicly known about GPT-3 (175B parameter version): - Number of non-e…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

79. Claude Opus 4.8 Anthropic, 2026년 5월 28일
    Estimating FLOPs to Train GPT-3 Key Facts I Recall - Parameters (N): GPT-3 has 175 billion parameters (1.75 × 10¹¹) - Training tokens (D): GPT-3 was t…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

80. Qwen3.7 Max Qwen, 2026년 5월 22일
    Estimating the FLOPs to Train GPT-3 Key Known Facts Property Value ------ Parameters (N) 175 billion (1.75 × 10¹¹) Training tokens (D) 300 billion (3 …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

81. Gemini 3.5 Flash Google, 2026년 5월 19일
    To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we can use a well-established heuristic in deep learning. H…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

82. ERNIE 4.5 300B A47B Baidu, 2026년 6월 퇴역, 2026년 5월 11일
    Here's a step - by - step estimation of the total number of FLOPs required to train GPT - 3: 1. Understand the key components of GPT - 3 training The …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

83. Ring 2.6 1T inclusionAI, 2026년 5월 퇴역, 2026년 5월 8일
    Step‑by‑step estimate Quantity Value (order of magnitude) How it is obtained ---------------------------------------------------------- Number of trai…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

84. Gemini 3.1 Flash Lite Google, 2026년 5월 7일
    To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we can use the standard scaling laws established by OpenAI …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

85. Grok 4.3 xAI, 2026년 5월 2일
    Step-by-step estimation: 1. GPT-3 has 175 billion parameters, so \(N = 1.75 \times 10^{11}\). 2. Training processed roughly 300 billion tokens in tota…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

86. Owl Alpha Code names, 2026년 6월 퇴역, 2026년 4월 28일
    To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we can use a well-known approximation from the literature a…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

87. Qwen3.6 Max Preview Qwen, 2026년 4월 27일
    Here’s a step-by-step estimation of the total FLOPs required to train GPT-3, using only first principles and widely known facts from the LLM training …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

88. Qwen3.6 Flash Qwen, 2026년 4월 27일
    Here’s a step-by-step derivation of the total FLOPs required to train GPT-3, using publicly known architectural specifications and standard deep learn…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

89. Qwen3.6 35B A3B Qwen, 2026년 4월 27일
    Here’s a step-by-step reasoning process to estimate the total FLOPs required to train GPT-3, using only established architectural principles and widel…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

90. Qwen3.6 27B Qwen, 2026년 4월 27일
    Here’s a step-by-step estimation of the total FLOPs required to train GPT-3, using publicly known model specifications and standard deep learning comp…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

91. Qwen3.5 Plus 2026-04-20 Qwen, 2026년 4월 27일
    Based on the architectural specifications of GPT-3 and standard computational heuristics for transformer models, the estimated total number of FLOPs (…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

92. GPT-5.5 OpenAI, 2026년 4월 24일
    A reasonable estimate for the compute to train GPT-3 is: \[ \boxed{\sim 3 \times 10^{23}\ \text{FLOPs}} \] More specifically, about: \[ \boxed{3.1 \ti…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

93. DeepSeek V4 Pro DeepSeek, 2026년 4월 24일
    To estimate the total number of FLOPs required to train GPT-3, we can use a well-known approximation for transformer models: Total training FLOPs ≈ 6 …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

94. DeepSeek V4 Flash DeepSeek, 2026년 4월 24일
    The total number of FLOPs required to train GPT-3 is approximately 3.15 × 10²³. This estimate is derived as follows: 1. Model parameters: GPT-3 has 17…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

95. Ling 2.6 1T inclusionAI, 2026년 5월 퇴역, 2026년 4월 23일
    Here is a step-by-step back-of-the-envelope estimate, using only widely known facts about GPT-3 and standard deep learning training heuristics (no onl…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

96. MiMo-V2.5-Pro Xiaomi, 2026년 4월 22일
    Estimating GPT-3 Training FLOPs Key Parameters Quantity Value ------ Model size (N) 175 billion parameters Training tokens (D) 300 billion tokens The …
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

97. MiMo-V2.5 Xiaomi, 2026년 4월 22일
    Estimating the total FLOPs (Floating Point Operations) required to train GPT-3 without external sources relies on a well-known approximation in deep l…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

98. Ling 2.6 Flash inclusionAI, 2026년 4월 퇴역, 2026년 4월 21일
    Alright — let's break this down step by step. --- Step 1: Understanding what “training GPT-3” means in FLOPs terms Training a large language model inv…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

99. Kimi K2.6 Moonshot, 2026년 4월 20일
    To estimate the training compute for GPT‑3, I’ll break the problem into three pieces: the size of the model, the size of the training dataset, and the…
    기록된 프롬프트

    Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

100. Claude Opus 4.7 Anthropic, 2026년 4월 16일
     Estimating GPT-3 Training FLOPs Step 1: Recall the standard formula For a dense transformer trained with standard autoregressive language modeling, th…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

101. Elephant Alpha Code names, 2026년 4월 퇴역, 2026년 4월 14일
     Alright — let’s break this down step by step. --- Step 1: Understanding the scale of GPT-3 GPT-3 has 175 billion parameters. Training involves optimiz…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

102. GLM 5.1 Zhipu, 2026년 4월 7일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established rule of thumb in deep learnin…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

103. Gemma 4 26B A4B Google, 2026년 4월 3일
     To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use a standard heuristic derived from the scaling la…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

104. Gemma 4 31B Google, 2026년 4월 2일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established heuristic from deep learning …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

105. Qwen3.6 Plus Preview Qwen, 2026년 4월 퇴역, 2026년 3월 30일
     Here’s a step-by-step estimation using well-established rules of thumb from transformer scaling literature. No external lookup is required; the deriva…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

106. Qwen3 Max Thinking Qwen, 2026년 3월 29일
     Step-by-Step Estimation of FLOPs to Train GPT-3 (All values derived from widely known technical details of GPT-3 as reported in OpenAI’s 2020 paper "L…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

107. Qwen3 30B A3B Thinking 2507 Qwen, 2026년 3월 29일
     Step-by-Step Estimation of GPT-3 Training FLOPs I'll estimate the total FLOPs (Floating Point Operations) required to train GPT-3 using only establish…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

108. Qwen3 30B A3B Qwen, 2026년 3월 29일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can break down the problem into key components: model ar…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

109. Qwen3 235B A22B Qwen, 2026년 3월 29일
     Step-by-Step Estimation of FLOPs Required to Train GPT-3 1. Determine the Number of Parameters (N): GPT-3 is known to have 175 billion parameters (1.7…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

110. Mistral Nemo Mistral, 2026년 3월 29일
     To estimate the total number of FLOPs (Floating Point Operations Per Second) required to train GPT-3, we'll break down the process into its key compon…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

111. Mistral Medium 3 Mistral, 2026년 3월 29일
     Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the problem into several key component…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

112. MiniMax M1 MiniMax, 2026년 3월 29일
     Below is a back‑of‑the‑envelope estimate of the total floating‑point operations (FLOPs) that were required to train GPT‑3 (the 175‑B parameter version…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

113. Llama 4 Scout Meta, 2026년 3월 29일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 without searching online, we'll need to make some educated g…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

114. Llama 4 Maverick Meta, 2026년 3월 29일
     Estimating the total number of FLOPs (Floating Point Operations) required to train a large language model like GPT-3 involves understanding the model'…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

115. Llama 3.1 70B (Instruct) Meta, 2026년 3월 29일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 without searching online, we'll break down the problem into …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

116. Llama 3 70B Meta, 2026년 6월 퇴역, 2026년 3월 29일
     What a delightful challenge! To estimate the total number of FLOPs (Floating-Point Operations Per Second) required to train GPT-3, I'll break down the…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

117. Gemma 3n 4B Google, 2026년 8월 퇴역, 2026년 3월 29일
     Okay, let's estimate the FLOPs required to train GPT-3 without relying on external online information. This will be an approximation based on publicly…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

118. GLM 4.5 Air Zhipu, 2026년 3월 29일
     To estimate the total FLOPs (Floating Point Operations) required to train GPT-3, we follow a step-by-step reasoning based on the model's architecture,…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

119. DeepSeek V3 0324 DeepSeek, 2026년 3월 29일
     Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves several steps and assumptions. Here's a step-by-step…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

120. DeepSeek R1 0528 DeepSeek, 2026년 3월 29일
     To estimate the total number of FLOPs required to train GPT-3, we use the standard formula for transformer model training FLOPs: Total FLOPs ≈ 6 × (nu…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

121. Claude Sonnet 4 Anthropic, 2026년 3월 29일
     I'll estimate the FLOPs required to train GPT-3 by breaking this down into components and making reasonable assumptions. Key Parameters and Assumption…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

122. Claude Opus 4.1 Anthropic, 2026년 3월 29일
     I'll estimate the FLOPs required to train GPT-3 by breaking down the computation into its key components. Key Parameters of GPT-3 First, let me recall…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

123. Claude Opus 4 Anthropic, 2026년 9월 퇴역, 2026년 3월 29일
     I'll estimate the FLOPs required to train GPT-3 by breaking this down into its components. Key Parameters I Need to Estimate: 1. Model Size: GPT-3 has…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

124. Claude 3 Haiku Anthropic, 2026년 9월 퇴역, 2026년 3월 29일
     Estimating the total number of FLOPs (Floating-Point Operations) required to train GPT-3 without searching online is a challenging task, as the exact …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

125. MiMo-V2-Pro Xiaomi, 2026년 5월 퇴역, 2026년 3월 19일
     Estimating GPT-3 Training FLOPs Key Parameters Component Value ------------------ Model parameters (N) 175 billion (1.75 × 10¹¹) Training tokens (D) 3…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

126. MiMo-V2-Omni Xiaomi, 2026년 5월 퇴역, 2026년 3월 19일
     To estimate the total floating-point operations (FLOPs) required to train GPT-3, we start with the model's key specifications: it has 175 billion para…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

127. MiniMax M2.7 MiniMax, 2026년 3월 18일
     To estimate the total FLOPs required to train GPT-3, we can follow a step-by-step reasoning process based on known architectural details and computati…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

128. Mistral Small 4 Mistral, 2026년 3월 17일
     Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 without external references involves breaking down the proble…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

129. GPT-5.4 Nano OpenAI, 2026년 3월 17일
     Below is a back-of-the-envelope FLOPs estimate for training something “GPT‑3–like” (the 175B parameter model described in the original GPT‑3 paper). I…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

130. GPT-5.4 Mini OpenAI, 2026년 3월 17일
     A reasonable back-of-the-envelope estimate for training GPT-3 is about 3 × 10²³ FLOPs total, with maybe a factor-of-2 uncertainty depending on what yo…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

131. GLM 5 Turbo Zhipu, 2026년 3월 16일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use the standard theoretical framework established b…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

132. Grok 4.20 Multi-Agent Beta xAI, 2026년 3월 퇴역, 2026년 3월 12일
     3.15 \times 10^{23} FLOPs GPT-3 (the 175B-parameter version) has N ≈ 1.75 × 10^{11} parameters. It was trained on a filtered dataset of roughly D ≈ 3 …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

133. Grok 4.20 Beta xAI, 2026년 3월 퇴역, 2026년 3월 12일
     Estimated total FLOPs for training GPT-3: 3.4 × 10²³ FLOPs Step-by-Step Reasoning 1. Model size (parameters) - GPT-3 (the 175B model) has 175 billion …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

134. NVIDIA Nemotron 3 Super NVIDIA, 2026년 3월 11일
     Step-by-Step Estimation of FLOPs for Training GPT-3 I will estimate the total floating-point operations (FLOPs) required to train GPT-3 based solely o…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

135. Hunter Alpha Code names, 2026년 3월 퇴역, 2026년 3월 11일
     Estimating the total FLOPs (floating-point operations) for training GPT‑3 involves combining known model parameters, training data size, and standard …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

136. Healer Alpha Code names, 2026년 3월 퇴역, 2026년 3월 11일
     Let me work through this step by step. Step 1: Understanding the Model Parameters GPT-3 has 175 billion parameters. This is the foundation for our cal…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

137. Seed 2.0 Lite ByteDance, 2026년 3월 10일
     Step-by-Step Estimate of GPT-3 Training FLOPs First, we ground the calculation in well-established properties of GPT-3 and transformer training comput…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

138. Qwen3.5 9B Qwen, 2026년 3월 10일
     Based on the available technical documentation and the original research paper "Language Models are Few-Shot Learners" (Brown et al., 2020), here is t…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

139. Mercury 2 Inception, 2026년 3월 5일
     Answer (order‑of‑magnitude estimate) The training of the original GPT‑3 (the 175 billion‑parameter “GPT‑3‑175B” model) requires on the order of 10²³ f…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

140. GPT-5.4 OpenAI, 2026년 3월 5일
     A standard back-of-the-envelope estimate for training FLOPs is: \[ \text{Training FLOPs} \approx 6 \times N \times D \] where: - \(N\) = number of mod…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

141. Gemini 3.1 Flash Lite Preview Google, 2026년 3월 3일
     To estimate the total number of FLOPs (Floating Point Operations) required to train a Large Language Model like GPT-3, we can use the standard formula…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

142. GPT-5.3 Chat OpenAI, 2026년 8월 퇴역, 2026년 3월 3일
     We can do a back‑of‑the‑envelope estimate using a common scaling rule for transformer training compute. 1) Known approximate GPT‑3 specs - Parameters …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

143. Qwen3.5 Flash Qwen, 2026년 2월 26일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can rely on the model's configuration details which were…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

144. Qwen3.5 35B A3B Qwen, 2026년 2월 26일
     To estimate the total FLOPs (Floating Point Operations) required to train GPT-3, we need to combine three key pieces of information: the number of par…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

145. Qwen3.5 27B Qwen, 2026년 2월 26일
     To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use the standard formula for estimating compute in T…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

146. Qwen3.5 122B A10B Qwen, 2026년 2월 26일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to rely on the known architectural hyperparameters …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

147. GPT-5.3-Codex OpenAI, 2026년 2월 25일
     A common back-of-the-envelope estimate for transformer training compute is: \[ \text{Training FLOPs} \approx 6 \times N \times D \] where: - \(N\) = n…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

148. Gemini 3.1 Pro Preview Google, 2026년 2월 19일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established heuristic in deep learning fo…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

149. Claude Sonnet 4.6 Anthropic, 2026년 2월 17일
     Estimating FLOPs to Train GPT-3 Step 1: Recall GPT-3's Architecture GPT-3 has approximately: - 175 billion parameters (this is well-known) - 96 transf…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

150. Qwen3.5 Plus 2026-02-15 Qwen, 2026년 2월 16일
     To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can derive the figure using the standard scaling laws fo…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

151. Qwen3.5 397B A17B Qwen, 2026년 2월 16일
     To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use the standard computational scaling laws for Tran…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

152. MiniMax M2.5 MiniMax, 2026년 2월 12일
     To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we use the standard approximation that training a transform…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

153. GLM 5 Zhipu, 2026년 2월 11일
     The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³. Step‑by‑step reasoning: 1. Model size: GPT‑3 has 175 billion (1.75 × 1…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

154. Aurora Alpha Code names, 2026년 2월 퇴역, 2026년 2월 9일
     Step‑by‑step back‑of‑the‑envelope estimate Below is a simple calculation that uses only publicly‑known high‑level facts about GPT‑3 (size of the model…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

155. Pony Alpha Code names, 2026년 2월 퇴역, 2026년 2월 6일
     The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³ (or 315 zettaFLOPs). The reasoning follows these steps: 1. Model size –…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

156. Qwen3 Coder Next Qwen, 2026년 2월 4일
     Estimating the total number of floating-point operations (FLOPs) to train GPT-3 without looking it up requires combining known architectural facts, sc…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

157. Claude Opus 4.6 Anthropic, 2026년 2월 4일
     Estimating FLOPs to Train GPT-3 Key Parameters Let me recall the key specs of GPT-3: - Parameters: 175 billion (1.75 × 10¹¹) - Training tokens: 300 bi…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

158. Trinity Large Preview Arcee, 2026년 5월 퇴역, 2026년 1월 30일
     To estimate the FLOPs for training GPT-3, I'll work through the key components: Model parameters: GPT-3 has 175 billion parameters. Architecture: GPT-…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

159. Solar Pro 3 Upstage, 2026년 1월 27일
     Answer – Rough estimate: ≈ 3 × 10²³ – 1 × 10²⁴ floating‑point operations (FLOPs) for the full GPT‑3 training run. Below is a step‑by‑step derivation t…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

160. MiniMax M2-her MiniMax, 2026년 1월 27일
     Alright, let's tackle this question. The user is asking to estimate the total number of FLOPs required to train GPT-3 without searching online. First,…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

161. Kimi K2.5 Moonshot, 2026년 1월 27일
     Here is a step-by-step estimate for the total training FLOPs of GPT-3. Step 1: Identify the key variables To estimate training compute for a dense tra…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

162. GLM 4.7 Flash Zhipu, 2026년 1월 27일
     Based on the known specifications of GPT-3 from its original training paper ("Scaling Laws for Neural Language Models"), here is the step-by-step esti…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

163. MiniMax M2.1 MiniMax, 2025년 12월 23일
     Below is a step‑by‑step reasoning that leads to a ball‑park estimate of the total floating‑point operations (FLOPs) required to train the largest GPT‑…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

164. GLM 4.7 Zhipu, 2025년 12월 23일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 (175 billion parameters), we can use the standard scaling la…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

165. Gemini 3 Flash Preview Google, 2025년 12월 17일
     To estimate the total floating-point operations (FLOPs) required to train GPT-3, we can use a standard heuristic derived from the architecture of Tran…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

166. Mistral Small Creative Mistral, 2026년 4월 퇴역, 2025년 12월 16일
     Estimating the total number of Floating-Point Operations (FLOPs) required to train GPT-3 (specifically, the 175B-parameter variant) involves breaking …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

167. MiMo-V2-Flash Xiaomi, 2026년 6월 퇴역, 2025년 12월 16일
     Of Of course. Here is a step-by-step estimation of the total number of FLOPs required to train GPT-3, based on the information available about its arc…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

168. GPT-5.2 Pro OpenAI, 2025년 12월 12일
     Below is a back-of-the-envelope FLOP estimate for training GPT‑3 (the 175B dense Transformer), using only basic Transformer compute facts and a few wi…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

169. GPT-5.2 Chat OpenAI, 2025년 12월 11일
     We can get a reasonable order‑of‑magnitude estimate using standard training‑cost heuristics that are well known in deep learning. I’ll derive it step …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

170. GPT-5.2 OpenAI, 2025년 12월 11일
     Below is a back-of-the-envelope estimate for the total training compute (in FLOPs) for GPT‑3, done from first principles and common Transformer traini…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

171. Devstral 2 2512 Mistral, 2025년 12월 9일
     Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 (175B parameters) from scratch involves several steps. Here's…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

172. GPT-5.1 Codex Max OpenAI, 2025년 12월 5일
     What you need for a back‑of‑the‑envelope is: How big the model is. The published “GPT‑3” has \(N\approx1.75\times10^{11}\) trainable weights. For a de…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

173. Amazon Nova 2 Lite Amazon, 2025년 12월 3일
     Estimating the Total FLOPs for Training GPT-3 To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we’ll break t…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

174. Mistral Large 3 2512 Mistral, 2025년 12월 2일
     Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the training process into its fundamen…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

175. DeepSeek V3.2 Speciale DeepSeek, 2026년 5월 퇴역, 2025년 12월 1일
     Estimating the total number of Floating Point Operations (FLOPs) required to train GPT-3 involves understanding the model's architecture, training pro…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

176. DeepSeek V3.2 DeepSeek, 2025년 12월 1일
     Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves understanding its architecture, training process, an…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

177. TNG R1T Chimera TNG, 2026년 2월 퇴역, 2025년 11월 27일
     To estimate the total number of FLOPs required to train GPT-3, we use the following reasoning: 1. FLOPs Formula: For transformer-based models, a commo…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

178. INTELLECT-3 Prime Intellect, 2026년 6월 퇴역, 2025년 11월 27일
     To estimate the total number of FLOPs required to train GPT-3, we need to consider the model architecture, training process, and dataset size. GPT-3 h…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

179. Claude Opus 4.5 Anthropic, 2025년 11월 24일
     I'll estimate the FLOPs required to train GPT-3 step by step, using what I know about the model architecture and training process. Key Parameters for …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

180. Bert-Nebulon Alpha Code names, 2026년 2월 퇴역, 2025년 11월 24일
     Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the training process into its core com…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

181. Grok 4.1 Fast xAI, 2026년 5월 퇴역, 2025년 11월 21일
     Step-by-Step Reasoning for Estimating GPT-3 Training FLOPs 1. Recall GPT-3 key specs from memory: - GPT-3 (davinci variant) has N = 175 billion parame…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

182. Gemini 3 Pro Preview Google, 2026년 3월 퇴역, 2025년 11월 18일
     To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a standard approximation formula derived from the architec…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

183. Sherlock Think Alpha Code names, 2026년 2월 퇴역, 2025년 11월 16일
     Step-by-Step Reasoning 1. Recall key specs of GPT-3: - GPT-3 has 175 billion parameters: \( N = 175 \times 10^9 = 1.75 \times 10^{11} \). - It was tra…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

184. Sherlock Dash Alpha Code names, 2026년 2월 퇴역, 2025년 11월 16일
     Step-by-Step Reasoning for Estimating GPT-3 Training FLOPs 1. Recall GPT-3 Key Specs (from Memory) - GPT-3 (175B parameter version): 175 billion param…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

185. GPT-5.1-Codex-Mini OpenAI, 2025년 11월 13일
     To estimate the total FLOPs required to train GPT-3, we can proceed with the following reasoning steps, using only basic information about the model’s…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

186. GPT-5.1-Codex OpenAI, 2025년 11월 13일
     To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a common approximation for transformer models: Total FLOPs…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

187. GPT-5.1 Chat OpenAI, 2026년 7월 퇴역, 2025년 11월 13일
     Here is a safe, self‑contained estimate based only on publicly known GPT‑3 facts and first‑principles reasoning. --- Goal: Estimate total training FLO…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

188. GPT-5.1 OpenAI, 2025년 11월 13일
     The total training compute for GPT‑3 (175B) is on the order of \(10^{23}\) floating‑point operations. A reasonable point estimate is \(\sim 3\times 10…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

189. Kimi Linear 48B A3B Instruct Moonshot, 2026년 2월 퇴역, 2025년 11월 10일
     Below is a back-of-the-envelope estimate of the total FLOPs needed to train GPT-3 “175 B” (the largest 96-layer, 12288-d model with 175 billion parame…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

190. Polaris Alpha Code names, 2026년 2월 퇴역, 2025년 11월 7일
     Great question—and nice constraint. I’ll estimate the total training FLOPs for GPT-3 from first principles, using only generally known architectural/t…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

191. Kimi K2 Thinking Moonshot, 2025년 11월 6일
     아무 말도 하지 않았습니다.
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

192. Sonar Pro Search Perplexity, 2025년 11월 3일
     Estimating the FLOPs Required to Train GPT-3 Background Information GPT-3 is a large transformer-based language model developed by OpenAI. It consists…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

193. Nova Premier 1.0 Amazon, 2025년 11월 3일
     Estimating the total number of FLOPs (floating point operations) required to train a model like GPT-3 involves considering several key factors: model …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

194. MiniMax M2 MiniMax, 2025년 10월 29일
     To estimate the total FLOPs required to train GPT-3 (175B parameters) on 300 billion tokens (one epoch), we break down the process step by step: Step …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

195. Claude Haiku 4.5 Anthropic, 2025년 10월 15일
     Estimating GPT-3 Training FLOPs I'll work through this from first principles using publicly known GPT-3 specs. Known Parameters - Model size: 175 bill…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

196. GPT-5 Pro OpenAI, 2025년 10월 6일
     Here’s a simple, standard way to estimate training FLOPs for a large dense Transformer like GPT-3: 1) Use the “6 N T” rule of thumb - For dense Transf…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

197. GLM 4.6 Zhipu, 2025년 10월 3일
     Of course. Estimating the FLOPs for training a model like GPT-3 without looking up the exact number is a fantastic exercise in reasoning from first pr…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

198. DeepSeek V3.2 Exp DeepSeek, 2025년 9월 29일
     Alright — let’s estimate the total FLOPs required to train GPT‑3 without looking anything up. --- 1. Known basic formula A common rule-of-thumb for tr…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

199. Claude Sonnet 4.5 Anthropic, 2025년 9월 29일
     I'll work through this estimation step by step, using what I know about GPT-3's architecture and training. Known/Estimated Parameters Model size: - GP…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

200. Gemini 2.5 Flash Preview 09-2025 Google, 2026년 2월 퇴역, 2025년 9월 26일
     Estimating the Total Number of FLOPs Required to Train GPT-3 Based on publicly available information about large language models (LLMs) and the specif…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

201. Gemini 2.5 Flash Lite Preview 09-2025 Google, 2026년 7월 퇴역, 2025년 9월 26일
     Estimating the total number of FLOPs required to train a large language model like GPT-3 without searching online requires recalling or estimating the…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

202. GPT-5 Codex OpenAI, 2026년 8월 퇴역, 2025년 9월 23일
     Step 1 – Note the basic training quantities - GPT‑3’s largest model has about \(P \approx 1.75 \times 10^{11}\) trainable parameters (175 billion). - …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

203. Grok 4 Fast xAI, 2026년 5월 퇴역, 2025년 9월 20일
     Step-by-Step Reasoning for Estimating GPT-3 Training FLOPs To estimate the total floating-point operations (FLOPs) required to train GPT-3, I'll rely …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

204. Qwen3 Coder Plus Qwen, 2025년 9월 17일
     I'll estimate the FLOPs required to train GPT-3 by breaking this down into key components. Key Parameters of GPT-3 - Parameters: 175 billion (1.75 × 1…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

205. Qwen3 Coder Flash Qwen, 2025년 9월 17일
     I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable assumptions. Step 1: Estimate Model Size GPT…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

206. Qwen3 Next 80B A3B Thinking Qwen, 2025년 9월 12일
     To estimate the total FLOPs (floating-point operations) required to train GPT-3 without external references, I'll break this down step by step using f…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

207. Qwen3 Next 80B A3B Instruct Qwen, 2025년 9월 12일
     Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 without searching online requires combining known architectur…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

208. Qwen Plus 0728 Qwen, 2025년 9월 12일
     To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we can use a widely adopted approximation based on the mode…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

209. NVIDIA Nemotron Nano 9B V2 NVIDIA, 2026년 8월 퇴역, 2025년 9월 12일
     Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves breaking down the computational complexity of the mo…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

210. Qwen Plus 0728 (thinking) Qwen, 2025년 9월 11일
     To estimate the total FLOPs (floating-point operations) required to train GPT-3, I'll use first-principles reasoning based on standard deep learning t…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

211. Sonoma Sky Alpha Code names, 2026년 2월 퇴역, 2025년 9월 5일
     Below, I'll provide a step-by-step estimation of the total floating-point operations (FLOPs) required to train GPT-3, based on my built-in knowledge o…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

212. Sonoma Dusk Alpha Code names, 2026년 2월 퇴역, 2025년 9월 5일
     Step 1: Recalling GPT-3's Key Parameters GPT-3 is a large language model with 175 billion parameters (weights). This is the standard figure I recall f…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

213. Qwen3 Max Qwen, 2025년 9월 5일
     Estimating the FLOPs required to train GPT-3 without looking it up is a classic “Fermi estimation” problem — breaking it down into components we can r…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

214. Kimi K2 0905 Moonshot, 2025년 9월 5일
     Below is a “back-of-the-envelope” that stays within a factor of 2 of the number most practitioners quote (≈3×10²³ FLOP). We will do it twice—once with…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

215. Grok Code Fast 1 xAI, 2026년 5월 퇴역, 2025년 8월 26일
     Step-by-Step Reasoning 1. Model Parameters: GPT-3 has approximately 175 billion parameters. This is a key factor as the computational cost scales with…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

216. DeepSeek V3.1 DeepSeek, 2025년 8월 21일
     Of course. This is an excellent exercise in understanding the computational scale of modern AI. Here is a step-by-step reasoning to estimate the FLOPs…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

217. Mistral Medium 3.1 Mistral, 2025년 8월 13일
     Estimating the total number of FLOPs (floating-point operations) required to train a model like GPT-3 from scratch involves breaking down the problem …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

218. GPT-5 Nano OpenAI, 2025년 8월 7일
     Here’s a back-of-the-envelope estimate for the total FLOPs to train GPT-3 (the 175B-parameter model) using reasonable public details, without looking …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

219. GPT-5 Mini OpenAI, 2025년 8월 7일
     Goal: estimate total floating-point operations (FLOPs) required to train GPT‑3 (the 175B‑parameter model). I’ll state assumptions, derive the FLOPs pe…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

220. GPT-5 OpenAI, 2025년 8월 7일
     Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics. 1) Assumptions - Model size (N): GPT-3 has about 175 billion par…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

221. GPT OSS 20B OpenAI, 2025년 8월 5일
     Below is a step‑by‑step, “hand‑calculation” of how many floating‑point operations (FLOPs) GPT‑3 had to do to learn from its training data. I’ll show t…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

222. GPT OSS 120B OpenAI, 2025년 8월 5일
     Quick answer: Training the original 175‑billion‑parameter GPT‑3 is estimated to have required on the order of 10²³ floating‑point operations (≈ 3 × 10…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

223. Horizon Beta Code names, 2026년 2월 퇴역, 2025년 8월 2일
     Here’s a back-of-the-envelope estimate for the total training compute (in FLOPs) for GPT-3, using standard scaling heuristics and reasonable assumptio…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

224. Horizon Alpha Code names, 2026년 2월 퇴역, 2025년 7월 31일
     Here’s a back‑of‑the‑envelope estimate using standard scaling heuristics and typical training setups for GPT‑3–class models. Assumptions 1) Model size…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

225. Qwen3 30B A3B Instruct 2507 Qwen, 2025년 7월 30일
     Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 without external sources involves using known facts about the…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

226. GLM 4 32B Zhipu, 2026년 6월 퇴역, 2025년 7월 29일
     Okay, let's estimate the FLOPs for training GPT-3 based on its known parameters and general understanding of Transformer training. GPT-3 Key Parameter…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

227. GLM 4.5 Zhipu, 2025년 7월 28일
     To estimate the total number of FLOPs (floating-point operations) required to train GPT-3 without searching online, we rely on known architectural det…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

228. Qwen3 235B A22B Thinking 2507 Qwen, 2025년 7월 25일
     To estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without external research, I'll rely on fundamental principles of tran…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

229. Qwen3 Coder Qwen, 2025년 7월 23일
     I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable approximations. Key Parameters of GPT-3 - Pa…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

230. Qwen3 235B A22B 2507 Qwen, 2026년 2월 퇴역, 2025년 7월 21일
     Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves reasoning through several key parameters: model size…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

231. Gemma 3 27B Google, 2025년 7월 21일
     Okay, let's estimate the FLOPs required to train GPT-3. This is a back-of-the-envelope calculation, relying on publicly available information and reas…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

232. Gemma 3 12B Google, 2025년 7월 21일
     Okay, let's estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without relying on external searches. This will be a back-of-…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

233. Kimi K2 Moonshot, 2025년 7월 12일
     Here is one way to “Fermi-estimate” the training compute for GPT-3 without looking up any numbers. I will start from generally known facts (model size…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

234. Mistral Devstral Small 1.1 Mistral, 2026년 5월 퇴역, 2025년 7월 11일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we'll break down the process into several steps and make re…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

235. Mistral Devstral Medium Mistral, 2026년 5월 퇴역, 2025년 7월 11일
     To estimate the total number of FLOPs (floating point operations) required to train GPT-3, we'll use the following steps: 1. Model size: GPT-3 has 175…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

236. Grok 4 xAI, 2026년 5월 퇴역, 2025년 7월 10일
     Below, I'll estimate the total number of floating-point operations (FLOPs) required to train GPT-3 from scratch, based on publicly known details about…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

237. Cypher Alpha Code names, 2026년 2월 퇴역, 2025년 7월 1일
     I cannot provide a specific estimate of the total number of FLOPs required to train GPT-3, as this information is proprietary and not publicly disclos…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

238. Mercury Inception, 2026년 4월 퇴역, 2025년 6월 26일
     I'll estimate the FLOPs needed to train GPT-3 using first principles and known parameters. Step 1: GPT-3 Model Parameters - GPT-3 has approximately 17…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

239. Gemini 2.5 Pro Preview 06-05 Google, 2026년 9월 퇴역, 2025년 6월 5일
     Of course. Here is an estimate of the total number of FLOPs required to train GPT-3, based on my internal kn owledge and first principles, without sea…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

240. Mistral Large Mistral, 2025년 5월 9일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we\'ll break down the calculation into several steps, consi…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

241. Gemini 2.5 Pro (I/O Edition) Google, 2026년 9월 퇴역, 2025년 5월 6일
     Okay, let's estimate the FLOPs for training GPT-3. I'll break this down. Key Formula: The number of FLOPs for training a transformer-based model can b…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

242. Gemini 1.5 Pro Google, 2026년 2월 퇴역, 2025년 4월 26일
     Let\'s break down estimating the FLOPs for GPT-3 training. Since we can\'t look up exact figures, we\'ll rely on reasonable guesses and approximations…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

243. PaLM 2 Chat Google, 2026년 2월 퇴역, 2025년 4월 20일
     To estimate the total number of FLOPs required to train GPT-3 without searching online, we can make several assumptions and perform calculations based…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

244. Gemini Pro 1.0 Google, 퇴역, 2025년 4월 20일
     Estimating GPT-3 Training FLOPs: A Step-by-Step Approach Without access to online resources, we can estimate the FLOPs (Floating-point Operations) nee…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

245. Gemini 2.5 Flash Preview Google, 2026년 2월 퇴역, 2025년 4월 17일
     Let's estimate the FLOPs required to train GPT-3 without looking it up. We need to break this down into the key components: the model size and the amo…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

246. GPT-3.5 Turbo OpenAI, 2025년 4월 17일
     GPT-3 is one of the largest language models ever created with 175 billion parameters. The number of FLOPs required to train a neural network depends o…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

247. OpenAI o4-mini OpenAI, 2025년 4월 16일
     Here's a back‐of‐the‐envelope derivation that gets us into the right ballpark (∼10²³ FLOPs total): 1. Model size • GPT–3 "175 B" has about P ≃1.75×10¹…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

248. OpenAI o4 Mini High OpenAI, 2025년 4월 16일
     Here is one way to get to the oft‐quoted figure of "a few × 10^23 FLOPs" for training GPT‑3. I will make rough but plausible assumptions and carry thr…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

249. OpenAI o3 OpenAI, 2025년 4월 16일
     Below is a simple "back-of-the-envelope" estimate that uses only a few public facts about GPT-3 and very generic rules of thumb for counting operation…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

250. GPT-4.1 Nano OpenAI, 2025년 4월 14일
     Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3, based on what is known about its size, training data, an…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

251. GPT-4.1 Mini OpenAI, 2025년 4월 14일
     Certainly! Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 by breaking down the problem step-by-step. We …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

252. GPT-4.1 OpenAI, 2025년 4월 14일
     Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3. Step 1: Recall GPT-3 scale From published sources (I won…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

253. Grok 3 Beta xAI, 2026년 5월 퇴역, 2025년 4월 13일
     Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 is a complex task, but I can provide a rough estimate by brea…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

254. Optimus Alpha Code names, 퇴역, 2025년 4월 11일
     Let's walk through an estimation of how many FLOPs were required to train GPT-3, using only reasoning and not searching for the exact numbers. Step 1:…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

255. GPT-4o mini OpenAI, 2025년 4월 11일
     To estimate the total number of FLOPs (floating-point operations) required to train GPT-3, we can break down the computation involved in the training …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

256. Grok 3 Thinking xAI, 2025년 3월 1일
     To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

257. Grok 3 xAI, 2026년 5월 퇴역, 2025년 3월 1일
     To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we’ll need to make some reasonable assumptions based on wha…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

258. GPT-4.5 OpenAI, 2026년 2월 퇴역, 2025년 3월 1일
     Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 step by step, using reasonable assumptions and approximat…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

259. o3 Mini OpenAI, 2025년 2월 28일
     Below is a step‐by‐step estimation: 1. Parameters Count: GPT‑3 has about 175 billion parameters, i.e. roughly 1.75×10¹¹ parameters. 2. Training Data: …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

260. o1 OpenAI, 2025년 2월 28일
     Below is one way to arrive at a back-of-the-envelope estimate for the total floating-point operations (FLOPs) needed to train GPT-3 (the 175B-paramete…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

261. GPT-4o (Omni) OpenAI, 2025년 2월 28일
     To estimate the total number of FLOPs required to train GPT-3, let's break it down into key components. 1. Model Size and Parameters GPT-3 has 175 bil…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

262. Gemini 2.0 Flash Thinking Google, 퇴역, 2025년 2월 27일
     Estimating the total FLOPs for training GPT-3 without searching online requires making some educated assumptions and using scaling laws and general kn…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

263. DeepSeek R1 DeepSeek, 2025년 2월 27일
     To estimate the total number of FLOPs required to train GPT-3, we use the following reasoning: 1. Model Parameters (P): GPT-3 has 175 billion paramete…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

264. Claude 3.7 Sonnet Anthropic, 2026년 5월 퇴역, 2025년 2월 27일
     Estimating FLOPs for GPT-3 Training I'll estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 by breaking this down …
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

265. Claude 3.5 Sonnet Anthropic, 2026년 4월 퇴역, 2025년 2월 26일
     I'll help you estimate the FLOPs (Floating Point Operations) required to train GPT-3 from first principles. Let's break this down step by step. 1. Mod…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

266. Claude 3.7 Thinking Sonnet Anthropic, 2026년 5월 퇴역, 2025년 2월 26일
     Estimating GPT-3 Training FLOPs I'll estimate the total number of floating point operations (FLOPs) required to train GPT-3 by breaking down the calcu…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

267. Gemini 2.0 Pro Experimental Google, 퇴역, 2025년 1월 1일
     Okay, let's break down this estimation step-by-step, making some reasonable assumptions along the way, since we can't look up the exact details. We'll…
     기록된 프롬프트

     Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

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