❌Won't fitfp16 · 4K ctx · batch 1
Does A100 40GB run Qwen2.5-72B? No at fp16.
At fp16 precision, Qwen2.5-72B (72 B parameters) needs roughly 164.1 GB of VRAM. A100 40GB only has 40 GB, short by 124.1 GB. INT4 quantization brings it down to 44.6 GB.
410%
VRAM
Won't fit
Estimated VRAM (fp16)
164.1 GB
of 40 GB on A100 40GB· 124.1 GB short
Tok/sec
27
TFTT
2455ms
Power
255W
Rent on GPUniq marketplace
A100 40GB
Live prices from verified providers · billed hourly, no commitment.
Memory breakdown (fp16)
Model Weights
144.00 GBKV Cache
3.75 GBActivations
15.00 GBFramework Overhead
1.36 GBQuantization comparison
Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.
| Precision | VRAM | Utilisation | Fits on A100 40GB? |
|---|---|---|---|
| FP16 (full precision) | 164.1 GB | 410% | ❌Won't fit |
| INT8 (8-bit) | 84.4 GB | 211% | ❌Won't fit |
| INT4 / Q4 (4-bit) | 44.6 GB | 111% | ❌Won't fit |
Frequently asked
- Can A100 40GB run Qwen2.5-72B?
- Not at fp16 — Qwen2.5-72B needs about 164.1 GB while A100 40GB has 40 GB. It fits at INT4 quantization (44.6 GB) with some accuracy trade-off.
- How much VRAM does Qwen2.5-72B use?
- About 164.1 GB at fp16, 44.6 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
- What's the fastest way to run Qwen2.5-72B on A100 40GB?
- Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For A100 40GB, enable Flash Attention 2.
- Where can I rent a A100 40GB?
- GPUniq aggregates live A100 40GB offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
Other GPUs for Qwen2.5-72B
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None of the common quantization levels fit this model on A100 40GB. Consider multi-GPU deployment or a larger card.