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How to reproduce Deep Infer prefill and decode benchmarks with Deep Infer

Run the sweep script to measure prefill and decode performance on SM100 GPUs.

Who it is forAn infrastructure engineer comparing serving hardware or validating the published Deep Infer performance numbers.

The problemPublished benchmark numbers are only useful if you can rerun them on your own setup.

The outcomeYour own prefill and decode benchmark results that you can compare with the published charts.

What you need first

  • Linux with GCC 13.0 or newer and Python 3.12
  • NVIDIA SM100 GPU with CUDA 13, or Huawei Ascend 950 NPU with CANN 9.2
  • Access to the deepseek-ai/DeepSeek-V4.1-Flash-1017 checkpoint on Hugging Face, or a local copy
  • Enough disk space and memory for the checkpoint and the build

Steps

  1. Complete the build steps so Deep Infer runs on your SM100 GPUs.
  2. Read the README note that prefill was measured on 4 SM100 GPUs and decode on 64, each within one scale-up domain.
  3. Run the sweep script at examples/sweep/run_sweep.sh.
  4. Save the output and compare the prefill and decode results with the charts in the README.

Prompt for your agent

In the DeepInfer repo, run examples/sweep/run_sweep.sh on <number of GPUs> SM100 GPUs and save the output to <output path>. Report the prefill and decode results. Verify that the GPU count matches the layout the README describes before comparing with the published charts.

Caveat. The published numbers used 4 GPUs for prefill and 64 for decode within one scale-up domain, so a smaller setup will not match the charts directly.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for deepseek-ai/DeepInfer and "Reproduce Deep Infer prefill and decode benchmarks"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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