✅Runs easilyfp16 · 4K ctx · batch 1

Run Qwen3-8B on A100 40GB

At fp16 precision, Qwen3-8B (8.0 B parameters) needs roughly 20.2 GB of VRAM. A100 40GB has 40 GB, leaving 19.8 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 82 tokens/sec on a single card.

50%
VRAM
Runs easily
Estimated VRAM (fp16)
20.2 GB
of 40 GB on A100 40GB· 19.8 GB free
Tok/sec
82
TFTT
818ms
Power
153W
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A100 40GB
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Memory breakdown (fp16)

Model Weights
16.00 GB
KV Cache
0.63 GB
Activations
2.50 GB
Framework Overhead
1.04 GB

Quantization comparison

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

PrecisionVRAMUtilisationFits on A100 40GB?
FP16 (full precision)20.2 GB50%✅Runs easily
INT8 (8-bit)10.9 GB27%✅Runs easily
INT4 / Q4 (4-bit)6.3 GB16%✅Runs easily

Frequently asked

Can A100 40GB run Qwen3-8B?
Yes. Qwen3-8B needs ≈ 20.2 GB VRAM at fp16 and A100 40GB provides 40 GB. Expected throughput is 82 tokens/sec per GPU.
How much VRAM does Qwen3-8B use?
About 20.2 GB at fp16, 6.3 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 Qwen3-8B 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 Qwen3-8B

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Send a prompt and see how Qwen3-8B responds directly in our chat. No installation, no GPU required to test.

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