FLOPs to train GPT-3: 265 answers
“Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.”
Every answer the museum has to this question, newest first, from Jan 2025 to Oct 2026.
- Mistral Large 4
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…
- Ling 3.1 Flash
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…
- GPT-6.1 Sol
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…
- Ternary Bonsai 2 27B
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…
- Solar Pro 4
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)…
- Solar Mini 4
Estimating GPT-3 Training FLOPs To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I …
- Qwen3.8 Omni Flash
Assuming you mean the full 175B-parameter GPT-3 model’s main training run, a good estimate is: \[ \boxed{\text{about }3\times 10^{23}\text{ FLOPs}} \]…
- Qwen3.8 Max Prime
Estimating FLOPs to Train GPT-3 Key Facts About GPT-3 Property Value ----------------- Parameters (N) 175 billion Training tokens (D) 300 billion Arch…
- Qwen3.8 Max (0902)
Estimating the FLOPs to Train GPT-3 Key Facts About GPT-3 Property Value ----------------- Parameters (N) 175 billion Training tokens (D) 300 billion …
- Qwen3.8 Flash
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 …
- Qwen3.7 Flash
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 …
- Nemotron 3 Nano Omni
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…
- Muse Glimmer 30B
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…
- MiMo-V2.6-Pro
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…
- MiMo-V2.6-Flash
Estimating GPT-3 Training FLOPs Known Facts About GPT-3 - Parameters (N): 175 billion (175 × 10⁹) - Training tokens (D): 300 billion (300 × 10⁹) - Arc…
- Mercury 2.5
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…
- Ling 3.0 Flash Sante
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…
- Ling 3.0 Flash Fin
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…
- Ling 3.0 Flash
Estimating GPT-3 Training FLOPs from First Principles Key Known Facts About GPT-3 (from memory/prior knowledge) Parameter Value ------ Number of param…
- Laguna XS 2.1
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…
- Laguna S 2.1
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…
- Hy3
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…
- Grok 4.7
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…
- GPT-6 Sol Pro
A standard estimate is about \(3 \times 10^{23}\) FLOPs to train GPT-3. 1. GPT-3 has approximately 175 billion parameters: \(N = 1.75 \times 10^{11}\)…
- GPT-6 Sol
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…
- GPT-6 Luna Pro
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…
- GPT-6 Luna
A standard estimate for training a dense language model is \[ \text{training FLOPs} \approx 6ND, \] where \(N\) is the number of model parameters and …
- GLM 5.3 Prime
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…
- GLM 5.3 FlashX
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 …
- GLM 5.3 Flash
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…
- GLM 5.3
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…
- DeepSeek V4.1 Flash
To estimate GPT-3 training FLOPs from memory, use the standard dense-transformer training compute approximation: \[ C \approx 6ND \] where: - \(N\) = …
- DeepSeek V4 Flash Vision Exp
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…
- Command A+
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…
- Claude Sonnet 5.5
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 …
- Claude Opus 5.5
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 (…
- GPT-6 Astra
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…
- Muse Spark 1.3 Contributor
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…
- Muse Spark 1.3
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…
- Mercury 2.5 Preview
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…
- Hy4 Preview
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…
- Granite 4.2 8B
To estimate the total FLOPs for training GPT-3 without external data, follow these steps: 1. Identify key parameters: - Model size: 175B parameters (G…
- Gemini 3.8 Flash
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…
- Ox Alpha
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…
- Seed 2.1 Turbo
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…
- Seed 2.0 Code
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…
- Qwen3.8 27B
A good estimate uses the standard LLM training compute rule of thumb: \[ \text{Training FLOPs} \approx 6ND \] where: - \(N\) = number of model paramet…
- Qwen3.8 2.4T A95B
A reasonable estimate is: \[ \boxed{\sim 3 \times 10^{23} \text{ FLOPs}} \] More specifically, about: \[ \boxed{3.1 \times 10^{23} \text{ FLOPs}} \] f…
- Nemotron 3.5 Lightning
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…
- LFM2.5-2.6B
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…
- Grok 4.6
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…
- Gemini 3.7 Flash
To estimate the total floating-point operations (FLOPs) required to train GPT-3, we can use the standard scaling laws and computational approximations…
- Dots3-Note Preview
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…
- DeepSeek V4 Pro 0813
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…
- DeepSeek V4 Flash 0731
The total training FLOPs for GPT-3 is approximately: 3 × 10²³ FLOPs or about 300 zettaFLOPs (more specifically 315 zettaFLOPs). Step-by-step reasoning…
- Claude Opus 5
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…
- Gemini 3.6 Flash
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…
- Inkling
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 …
- Muse Spark 1.1
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…
- Kimi K3
Assume “GPT-3” refers to the flagship 175-billion-parameter model. 1. Use the standard transformer training-cost approximation For a dense transformer…
- Grok 4.5
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…
- GPT-5.6 Terra
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…
- GPT-5.6 Sol
A standard back-of-the-envelope estimate for dense Transformer training is: \[ C \approx 6ND \] where: - \(N\) = number of trainable parameters - \(D\…
- GPT-5.6 Luna Pro
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 = …
- GPT-5.6 Luna
A standard back-of-the-envelope estimate for training a dense Transformer is \[ \text{Training FLOPs} \approx 6ND, \] where: - \(N\) = number of model…
- Claude Sonnet 5
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…
- North Mini Code
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 …
- GLM 5.2
Based on standard machine learning scaling laws and the known architecture of GPT-3, the estimated total number of FLOPs (Floating Point Operations) r…
- OpenRouter Fusion · Quality (Jun 2026)
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…
- OpenRouter Fusion · Budget (Jun 2026)
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…
- Kimi K2.7 Code
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…
- Claude Fable 5
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…
- Nemotron 3.5 Content Safety
User Safety: safe
- Nemotron 3 Ultra
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…
- Qwen3.7 Plus
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…
- MiniMax M3
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…
- Claude Opus 4.8
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…
- Qwen3.7 Max
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 …
- Gemini 3.5 Flash
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…
- ERNIE 4.5 300B A47B
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 …
- Ring 2.6 1T
Step‑by‑step estimate Quantity Value (order of magnitude) How it is obtained ---------------------------------------------------------- Number of trai…
- Gemini 3.1 Flash Lite
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 …
- Grok 4.3
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…
- Owl Alpha
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…
- Qwen3.6 Max Preview
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 …
- Qwen3.6 Flash
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…
- Qwen3.6 35B A3B
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…
- Qwen3.6 27B
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…
- Qwen3.5 Plus 2026-04-20
Based on the architectural specifications of GPT-3 and standard computational heuristics for transformer models, the estimated total number of FLOPs (…
- GPT-5.5
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…
- DeepSeek V4 Pro
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 …
- DeepSeek V4 Flash
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…
- Ling 2.6 1T
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…
- MiMo-V2.5-Pro
Estimating GPT-3 Training FLOPs Key Parameters Quantity Value ------ Model size (N) 175 billion parameters Training tokens (D) 300 billion tokens The …
- MiMo-V2.5
Estimating the total FLOPs (Floating Point Operations) required to train GPT-3 without external sources relies on a well-known approximation in deep l…
- Ling 2.6 Flash
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…
- Kimi K2.6
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…
- Claude Opus 4.7
Estimating GPT-3 Training FLOPs Step 1: Recall the standard formula For a dense transformer trained with standard autoregressive language modeling, th…
- Elephant Alpha
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…
- GLM 5.1
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…
- Gemma 4 26B A4B
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…
- Gemma 4 31B
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 …
- Qwen3.6 Plus Preview
Here’s a step-by-step estimation using well-established rules of thumb from transformer scaling literature. No external lookup is required; the deriva…
- Qwen3 Max Thinking
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…
- Qwen3 30B A3B Thinking 2507
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…
- Qwen3 30B A3B
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…
- Qwen3 235B A22B
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…
- Mistral Nemo
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…
- Mistral Medium 3
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the problem into several key component…
- MiniMax M1
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…
- Llama 4 Scout
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…
- Llama 4 Maverick
Estimating the total number of FLOPs (Floating Point Operations) required to train a large language model like GPT-3 involves understanding the model'…
- Llama 3.1 70B (Instruct)
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 …
- Llama 3 70B
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…
- Gemma 3n 4B
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…
- GLM 4.5 Air
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,…
- DeepSeek V3 0324
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…
- DeepSeek R1 0528
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…
- Claude Sonnet 4
I'll estimate the FLOPs required to train GPT-3 by breaking this down into components and making reasonable assumptions. Key Parameters and Assumption…
- Claude Opus 4.1
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…
- Claude Opus 4
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…
- Claude 3 Haiku
Estimating the total number of FLOPs (Floating-Point Operations) required to train GPT-3 without searching online is a challenging task, as the exact …
- MiMo-V2-Pro
Estimating GPT-3 Training FLOPs Key Parameters Component Value ------------------ Model parameters (N) 175 billion (1.75 × 10¹¹) Training tokens (D) 3…
- MiMo-V2-Omni
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…
- MiniMax M2.7
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…
- Mistral Small 4
Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 without external references involves breaking down the proble…
- GPT-5.4 Nano
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…
- GPT-5.4 Mini
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…
- GLM 5 Turbo
To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use the standard theoretical framework established b…
- Grok 4.20 Multi-Agent Beta
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 …
- Grok 4.20 Beta
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 …
- NVIDIA Nemotron 3 Super
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…
- Hunter Alpha
Estimating the total FLOPs (floating-point operations) for training GPT‑3 involves combining known model parameters, training data size, and standard …
- Healer Alpha
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…
- Seed 2.0 Lite
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…
- Qwen3.5 9B
Based on the available technical documentation and the original research paper "Language Models are Few-Shot Learners" (Brown et al., 2020), here is t…
- Mercury 2
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…
- GPT-5.4
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…
- Gemini 3.1 Flash Lite Preview
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…
- GPT-5.3 Chat
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 …
- Qwen3.5 Flash
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…
- Qwen3.5 35B A3B
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…
- Qwen3.5 27B
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…
- Qwen3.5 122B A10B
To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to rely on the known architectural hyperparameters …
- GPT-5.3-Codex
A common back-of-the-envelope estimate for transformer training compute is: \[ \text{Training FLOPs} \approx 6 \times N \times D \] where: - \(N\) = n…
- Gemini 3.1 Pro Preview
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…
- Claude Sonnet 4.6
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…
- Qwen3.5 Plus 2026-02-15
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…
- Qwen3.5 397B A17B
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…
- MiniMax M2.5
To estimate the total number of floating-point operations (FLOPs) required to train GPT-3, we use the standard approximation that training a transform…
- GLM 5
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…
- Aurora Alpha
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…
- Pony Alpha
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 –…
- Qwen3 Coder Next
Estimating the total number of floating-point operations (FLOPs) to train GPT-3 without looking it up requires combining known architectural facts, sc…
- Claude Opus 4.6
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…
- Trinity Large Preview
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-…
- Solar Pro 3
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…
- MiniMax M2-her
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,…
- Kimi K2.5
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…
- GLM 4.7 Flash
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…
- MiniMax M2.1
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‑…
- GLM 4.7
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…
- Gemini 3 Flash Preview
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…
- Mistral Small Creative
Estimating the total number of Floating-Point Operations (FLOPs) required to train GPT-3 (specifically, the 175B-parameter variant) involves breaking …
- MiMo-V2-Flash
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…
- GPT-5.2 Pro
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…
- GPT-5.2 Chat
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 …
- GPT-5.2
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…
- Devstral 2 2512
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 (175B parameters) from scratch involves several steps. Here's…
- GPT-5.1 Codex Max
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…
- Amazon Nova 2 Lite
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…
- Mistral Large 3 2512
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the training process into its fundamen…
- DeepSeek V3.2 Speciale
Estimating the total number of Floating Point Operations (FLOPs) required to train GPT-3 involves understanding the model's architecture, training pro…
- DeepSeek V3.2
Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves understanding its architecture, training process, an…
- TNG R1T Chimera
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…
- INTELLECT-3
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…
- Claude Opus 4.5
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 …
- Bert-Nebulon Alpha
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the training process into its core com…
- Grok 4.1 Fast
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…
- Gemini 3 Pro Preview
To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a standard approximation formula derived from the architec…
- Sherlock Think Alpha
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…
- Sherlock Dash Alpha
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…
- GPT-5.1-Codex-Mini
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…
- GPT-5.1-Codex
To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a common approximation for transformer models: Total FLOPs…
- GPT-5.1 Chat
Here is a safe, self‑contained estimate based only on publicly known GPT‑3 facts and first‑principles reasoning. --- Goal: Estimate total training FLO…
- GPT-5.1
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…
- Kimi Linear 48B A3B Instruct
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…
- Polaris Alpha
Great question—and nice constraint. I’ll estimate the total training FLOPs for GPT-3 from first principles, using only generally known architectural/t…
- Kimi K2 Thinking Said nothing.
- Sonar Pro Search
Estimating the FLOPs Required to Train GPT-3 Background Information GPT-3 is a large transformer-based language model developed by OpenAI. It consists…
- Nova Premier 1.0
Estimating the total number of FLOPs (floating point operations) required to train a model like GPT-3 involves considering several key factors: model …
- MiniMax M2
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 …
- Claude Haiku 4.5
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…
- GPT-5 Pro
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…
- GLM 4.6
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…
- DeepSeek V3.2 Exp
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…
- Claude Sonnet 4.5
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…
- Gemini 2.5 Flash Preview 09-2025
Estimating the Total Number of FLOPs Required to Train GPT-3 Based on publicly available information about large language models (LLMs) and the specif…
- Gemini 2.5 Flash Lite Preview 09-2025
Estimating the total number of FLOPs required to train a large language model like GPT-3 without searching online requires recalling or estimating the…
- GPT-5 Codex
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). - …
- Grok 4 Fast
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 …
- Qwen3 Coder Plus
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…
- Qwen3 Coder Flash
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…
- Qwen3 Next 80B A3B Thinking
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…
- Qwen3 Next 80B A3B Instruct
Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 without searching online requires combining known architectur…
- Qwen Plus 0728
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…
- NVIDIA Nemotron Nano 9B V2
Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 involves breaking down the computational complexity of the mo…
- Qwen Plus 0728 (thinking)
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…
- Sonoma Sky Alpha
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…
- Sonoma Dusk Alpha
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…
- Qwen3 Max
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…
- Kimi K2 0905
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…
- Grok Code Fast 1
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…
- DeepSeek V3.1
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…
- Mistral Medium 3.1
Estimating the total number of FLOPs (floating-point operations) required to train a model like GPT-3 from scratch involves breaking down the problem …
- GPT-5 Nano
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 …
- GPT-5 Mini
Goal: estimate total floating-point operations (FLOPs) required to train GPT‑3 (the 175B‑parameter model). I’ll state assumptions, derive the FLOPs pe…
- GPT-5
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…
- GPT OSS 20B
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…
- GPT OSS 120B
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…
- Horizon Beta
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…
- Horizon Alpha
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…
- Qwen3 30B A3B Instruct 2507
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 without external sources involves using known facts about the…
- GLM 4 32B
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…
- GLM 4.5
To estimate the total number of FLOPs (floating-point operations) required to train GPT-3 without searching online, we rely on known architectural det…
- Qwen3 235B A22B Thinking 2507
To estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without external research, I'll rely on fundamental principles of tran…
- Qwen3 Coder
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…
- Qwen3 235B A22B 2507
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves reasoning through several key parameters: model size…
- Gemma 3 27B
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…
- Gemma 3 12B
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-…
- Kimi K2
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…
- Mistral Devstral Small 1.1
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…
- Mistral Devstral Medium
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…
- Grok 4
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…
- Cypher Alpha
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…
- Mercury
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…
- Gemini 2.5 Pro Preview 06-05
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…
- Mistral Large
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…
- Gemini 2.5 Pro (I/O Edition)
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…
- Gemini 1.5 Pro
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…
- PaLM 2 Chat
To estimate the total number of FLOPs required to train GPT-3 without searching online, we can make several assumptions and perform calculations based…
- Gemini Pro 1.0
Estimating GPT-3 Training FLOPs: A Step-by-Step Approach Without access to online resources, we can estimate the FLOPs (Floating-point Operations) nee…
- Gemini 2.5 Flash Preview
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…
- GPT-3.5 Turbo
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…
- OpenAI o4-mini
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…
- OpenAI o4 Mini High
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…
- OpenAI o3
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…
- GPT-4.1 Nano
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…
- GPT-4.1 Mini
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 …
- GPT-4.1
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…
- Grok 3 Beta
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…
- Optimus Alpha
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:…
- GPT-4o mini
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 …
- Grok 3 Thinking
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…
- Grok 3
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…
- GPT-4.5
Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 step by step, using reasonable assumptions and approximat…
- o3 Mini
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: …
- o1
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…
- GPT-4o (Omni)
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…
- Gemini 2.0 Flash Thinking
Estimating the total FLOPs for training GPT-3 without searching online requires making some educated assumptions and using scaling laws and general kn…
- DeepSeek R1
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…
- Claude 3.7 Sonnet
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 …
- Claude 3.5 Sonnet
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…
- Claude 3.7 Thinking Sonnet
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…
- Gemini 2.0 Pro Experimental
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…