How Many GPUs to train gpt-5? All You Need to Know

Training a state-of-the-art large language model (LLM) like GPT-5 is a massive engineering, logistical, and financial undertaking. Headlines and rumors about how many GPUs were used vary wildly — from a few tens of thousands to several hundreds of thousands — and part of that variance comes from changing hardware generations, efficiency gains in software, and the fact that companies rarely publish full training telemetry. In this article I explain how the estimate is derived, and highlight the constraints that determine the final number.

How many GPUs does it take to train GPT-5?

Short answer up front: there is no single number. Public signals and technical scaling formulas put plausible answers anywhere from the low thousands (for a compact, time-flexible training run) up to the low hundreds of thousands if you insist on training a very large, dense model in a short window with commodity GPUs. Which end of that range you land on depends on model size, training compute budget (FLOPs), tokens used, per-GPU sustained throughput, time budget, and whether you use newer rack-scale Blackwell hardware or older A100/H100 machines. OpenAI says GPT-5 was trained on Microsoft Azure supercomputers (not a precise GPU count), and outside coverage and engineering back-of-envelope estimates provide the rest of the picture.

OpenAI (like most organizations) does not publish exact training FLOP counts or the raw GPU-hour ledger for its largest models, so we combine vendor specs, observed historical GPU usage patterns for prior models, and scaling laws to produce defensible ranges.

What basic rule ties model size to GPU count?

The core formula you can use

NVIDIA’s Megatron team provides a practical, widely used approximation for end-to-end training time: training_time (s)≈8⋅T⋅PN⋅X\text{training\_time (s)} \approx 8 \cdot \frac{T \cdot P}{N \cdot X}training_time (s)≈8⋅N⋅XT⋅P​

where:

• PPP = number of model parameters (weights)
• TTT = number of training tokens
• NNN = number of GPUs
• XXX = sustained per-GPU throughput (in FLOPs/sec, often expressed as teraFLOPs)
• the factor 8 comes from counting forward+backward + optimizer and other constants in the transformer FLOPs approximation.

Rearranged to estimate GPUs for a target schedule: N≈8⋅T⋅PX⋅training_time (s)N \approx 8 \cdot \frac{T \cdot P}{X \cdot \text{training\_time (s)}}N≈8⋅X⋅training_time (s)T⋅P​

This is the single most important engineering formula for converting a compute budget (FLOPs) into a GPU fleet size, and it’s the place we start any GPU-count estimate.

Important caveats

• “X” (sustained per-GPU TFLOPs) is the hardest number to pin down. Theoretical peak FLOPs (specs) are usually much higher than what a real training job achieves because of memory traffic, communication, and pipeline bubbles. NVIDIA reported an achieved throughput of ~163 TFLOPs per A100 GPU in an end-to-end large-model training experiment; H100 and Blackwell devices have much higher theoretical peaks, but achievable sustained throughput depends on software stack, model parallel configuration, and communication fabric. Use conservative achieved throughputs when budgeting.
• Token budget TTT is not standardized. NVIDIA used ~450B tokens for a 1-trillion parameter example; other teams use different token/parameter ratios (and synthetic tokens are increasingly used). Always state the token assumption explicitly.
• Memory and topology constraints (per-GPU memory, NVLink fabric, pipeline/tensor parallelism limits) can make certain GPU types better suited for large, tightly sharded models even if they have similar FLOP numbers. Rack-scale systems like NVIDIA’s GB300/GB300 NVL72 change the practical balance between FLOPs and memory.

How many GPUs did previous generations use

Historical anchors: GPT-3 and GPT-4 reporting

Industry reporting and technical commentary have repeatedly used reported GPU counts for earlier models to anchor estimates for later ones. Multiple credible outlets and industry observers estimate that GPT-4’s pretraining involved tens of thousands of A100 GPUs over weeks to months. For example, contemporaneous reporting put GPT-4’s training footprint in the ~10k–25k A100 range depending on whether one counts peak GPU inventory or GPUs concurrently active during pretraining. Those historical anchors are useful because they show the order of magnitude and how hardware generations (A100 → H100 / Blackwell) change throughput per device.

Implication: if GPT-4 used ~10k–25k A100s, then GPT-5—if larger by one or more orders of magnitude, or trained on more tokens—would require significantly more aggregate compute. But improvements in hardware (H100/Blackwell/TPU) and software (optimizer/precision/mixture-of-experts, data-efficiency) can reduce the number of physical devices needed to deliver the same or greater compute.

How many GPUs would you need for different GPT-5-scale scenarios?

Below I run three concrete scenario calculations—same method, different assumptions—so you can see how the GPU count moves with model size, hardware, and time budget. I state assumptions explicitly so you can repeat or adjust them.

Assumptions used (explicit)

1. Core FLOPs formula: N≈8⋅T⋅PX⋅timeN \approx 8 \cdot \frac{T \cdot P}{X \cdot \text{time}}N≈8⋅X⋅timeT⋅P​. (See NVIDIA Megatron.)
2. Token count scaling: I use NVIDIA’s example of ~450B tokens per 1T parameters (so T≈0.45⋅PT \approx 0.45 \cdot PT≈0.45⋅P) as a baseline and scale tokens linearly with parameters for these scenarios. That’s a plausible but not universal choice—some teams use more or fewer tokens per parameter.
3. Training window: 90 days (≈ 7,776,000 seconds). Shorter schedules require proportionally more GPUs; longer schedules require fewer.
4. Per-GPU sustained throughputs (X, TFLOPs): three pragmatic levels to show sensitivity:
   • Conservative / older A100-class achieved: 163 TFLOPs per GPU (NVIDIA’s measured achieved throughput in a 1T example).
   • Modern high-end H100-class effective throughput: ~600 TFLOPs (a conservative, achievable fraction of the H100 theoretical Tensor-core peaks after accounting for system-level inefficiencies).
   • Rack-scale Blackwell/GB300 effective: ~2,000 TFLOPs per GPU (represents aggressive, next-gen Blackwell/GB300 rack efficiencies and FP4/optimization benefits; real sustained numbers will vary by workload and topology).

Note: these X values are assumptions for an engineering illustration—use them as knobs you can change. The point is to show orders of magnitude.

Results (rounded)

Using the formula and the assumptions above, for a 90-day training run with tokens scaled as T=0.45⋅PT=0.45\cdot PT=0.45⋅P:

1 trillion parameters (1T):

• with 163 TFLOPs/GPU → ≈ 2,800 GPUs.
• with 600 TFLOPs/GPU → ≈ 770 GPUs.
• with 2,000 TFLOPs/GPU → ≈ 230 GPUs.

3 trillion parameters (3T):

• with 163 TFLOPs/GPU → ≈ 25,600 GPUs.
• with 600 TFLOPs/GPU → ≈ 6,900 GPUs.
• with 2,000 TFLOPs/GPU → ≈ 2,100 GPUs.

10 trillion parameters (10T):

• with 163 TFLOPs/GPU → ≈ 284,000 GPUs.
• with 600 TFLOPs/GPU → ≈ 77,000 GPUs.
• with 2,000 TFLOPs/GPU → ≈ 23,000 GPUs.

These show why people’s estimates vary so widely: a change in either per-GPU sustained throughput (hardware and software) or the desired time-to-train dramatically alters the GPU count. A model that’s ten times bigger requires ten-times more parameters PPP, and because tokens are typically scaled with model size too, total FLOPs (and hence GPU needs) grow superlinearly if you keep a fixed time budget.

Best-effort range for GPT-5 (synthesis):

• Lower bound (compute-efficient recipe + Blackwell/H100-class throughput): ~10,000–25,000 H100-equivalent GPUs deployed over months (if the model used significant algorithmic efficiency gains and smaller parameter count with aggressive data augmentation / fine-tuning).
• Central (plausible mainstream scenario): ~25,000–80,000 H100-equivalent GPUs (matching a step up from GPT-4’s reported tens-of-thousands to account for larger compute budgets and token counts).
• Upper bound (very large, multi-trillion parameter model trained with few algorithmic shortcuts): 80,000–150,000+ H100-equivalent GPUs at peak (if the team sought very short wall-clock time and used many devices in parallel).

These ranges are consistent with current vendor throughput, historical GPU usage for earlier models, and reported industry cluster sizes. They are estimates, not direct admissions from OpenAI. The exact number for GPT-5 remains proprietary.

What else adds to the GPU bill besides the raw pre-training run?

Factors that increase device count

• Ambition in parameter count and tokens: Doubling parameters usually implies comparable increases in tokens to remain compute-optimal.
• Desire for short wall-clock time: To complete training in weeks rather than months requires a proportional increase in concurrent GPU count.
• Large validation or RLHF regimes: Substantial post-training RLHF or human feedback cycles add meaningful GPU usage beyond the base pretraining FLOPs.
• Network and infrastructure inefficiencies: Poor interconnect scaling or low utilization inflates the number of physical GPUs needed to realize advertised throughput.

RLHF, fine-tuning, and evaluation

Reinforcement learning from human feedback (RLHF) phases, multi-stage fine-tuning, red-teaming runs, and large evaluation sweeps add substantial extra compute on top of “pre-training” FLOPs. These follow-on phases often require efficient policy training loops and repeated inference at scale (which is served on other GPU clusters), so the project GPU footprint is larger than the single pre-training estimate. OpenAI’s GPT-5 development explicitly references sophisticated safety and evaluation processes that add compute beyond pre-training.

Data generation and synthetic tokens

The scarcity of high-quality tokens at very large scales leads teams to generate synthetic tokens (self-play, model-generated continuations) which themselves require compute to produce and vet. Accounting for that pipeline increases the overall GPU and wall-clock compute used during a model project.

Serving fleet for launch and iteration

Launching a model to millions of users requires a large inference fleet separate from the training cluster. Reports that OpenAI had hundreds of thousands to a million+ GPUs online include serving capacity. That’s a different budget line than the training cluster, but it’s often conflated in public discussion.

Conclusion

There is no single definitive public number for “how many GPUs to train GPT-5” because the answer depends on the model’s parameterization, the training recipe, and whether priority is wall-clock time or total cost. Using public vendor specs, scaling-law research, and industry reporting as anchors, the most defensible public estimate is that GPT-5-class training likely required tens of thousands of H100-equivalent GPUs at peak (a plausible central range: ~25k–80k H100-equivalents), with aggregate GPU-hours in the multi-million range.

Where to Access GPT-5

If you want programmatic access or to embed GPT-5 Pro into products, use the API. OpenAI,CometAPI etc includes model names for the GPT-5 family (gpt-5-pro / gpt-5-pro-2025-10-06) and billing is per tokens used. The API enables advanced features like tool-enabled execution, longer context windows, streaming responses, and model parameters to control reasoning effort/verbosity.

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