How does 2×A100 40GB compare to H100 for Qwen3 32B?

In vLLM benchmarks, 2×A100 40GB achieved similar throughput (~976 tokens/s) and latency (median TTFT ~785ms) compared to H100, while being significantly more affordable.

What settings are required to run Qwen3 32B on dual A100?

Set --tensor-parallel-size=2 in your vLLM configuration, use NVLink for optimal inter-GPU communication, and monitor GPU temperature to prevent throttling. Also, set --max-model-len=4096 to avoid memory errors.

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Which GPU is the Cheapest for Qwen3-32B Inference with vLLM?

Q: Which GPU is the Cheapest for Qwen3 32B Inference with vLLM?

The cheapest GPU option for Qwen3 32B inference is a dual NVIDIA A100 40GB setup. Using vLLM with tensor-parallel-size set to 2, it delivers comparable performance to a single H100 at roughly half the price, achieving over 975 tokens/s in benchmark tests.

If you're looking to run inference on massive 32B models like Qwen-32B or Qwen3-32B from Hugging Face, the most cost-effective GPU setup for Qwen3-32B inference is 2×NVIDIA A100 40GB using vLLM with tensor-parallel-size=2.

Screenshot: 2*A100 40GB for Qwen3:32B with vLLM