✅Runs easilyfp16 · 4K ctx · batch 1

Run Qwen2.5-7B on A100 80GB

At fp16 precision, Qwen2.5-7B (7.0 B parameters) needs roughly 17.5 GB of VRAM. A100 80GB has 80 GB, leaving 62.5 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 95 tokens/sec on a single card.

22%
VRAM
Runs easily
Estimated VRAM (fp16)
17.5 GB
of 80 GB on A100 80GB· 62.5 GB free
Tok/sec
95
TFTT
705ms
Power
151W
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A100 80GB
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Memory breakdown (fp16)

Model Weights
14.00 GB
KV Cache
0.50 GB
Activations
2.00 GB
Framework Overhead
1.03 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)17.5 GB22%✅Runs easily
INT8 (8-bit)9.5 GB12%✅Runs easily
INT4 / Q4 (4-bit)5.5 GB7%✅Runs easily

Frequently asked

Can A100 80GB run Qwen2.5-7B?
Yes. Qwen2.5-7B needs ≈ 17.5 GB VRAM at fp16 and A100 80GB provides 80 GB. Expected throughput is 95 tokens/sec per GPU.
How much VRAM does Qwen2.5-7B use?
About 17.5 GB at fp16, 5.5 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 Qwen2.5-7B 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 Qwen2.5-7B

Other models on A100 80GB

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Send a prompt and see how Qwen2.5-7B responds directly in our chat. No installation, no GPU required to test.

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