❌Won't fitfp16 · 4K ctx · batch 1

Does A100 80GB run Qwen2.5-72B? No at fp16.

At fp16 precision, Qwen2.5-72B (72 B parameters) needs roughly 164.1 GB of VRAM. A100 80GB only has 80 GB, short by 84.1 GB. INT4 quantization brings it down to 44.6 GB — that fits.

205%
VRAM
Won't fit
Estimated VRAM (fp16)
164.1 GB
of 80 GB on A100 80GB· 84.1 GB short
Tok/sec
30
TFTT
2261ms
Power
255W
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Memory breakdown (fp16)

Model Weights
144.00 GB
KV Cache
3.75 GB
Activations
15.00 GB
Framework Overhead
1.36 GB

Quantization comparison

Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.

PrecisionVRAMUtilisationFits on A100 80GB?
FP16 (full precision)164.1 GB205%❌Won't fit
INT8 (8-bit)84.4 GB106%❌Won't fit
INT4 / Q4 (4-bit)44.6 GB56%✅Runs well

Frequently asked

Can A100 80GB run Qwen2.5-72B?
Not at fp16 — Qwen2.5-72B needs about 164.1 GB while A100 80GB has 80 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 80GB?
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 80GB, enable Flash Attention 2.
Where can I rent a A100 80GB?
GPUniq aggregates live A100 80GB 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

Other models on A100 80GB

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