Quantong Qiu 8e15ffe1c7 refactor: relocate old docs and add dsv4_h200_vllm experiment

- Move dspark_deepseekv4_fix_pr_prep.md into experiments/dsv4_h200_dspark/
- Move dsv4_inference_comparison_report.md into docs/
- Delete obsolete cleanup_summary.md
- Add experiments/dsv4_h200_vllm/ baseline experiment (envs/vllm + envs/sglang)

2026-07-08 06:19:40 +00:00

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DSV4 H200 vLLM Baseline Benchmark

NVIDIA H200 + native vllm + DeepSeek-V4-Flash baseline benchmark experiment.

This experiment uses:

/data/user1/yy/envs/vllm as the server environment
/data/user1/yy/envs/sglang as the benchmark client environment

Quick Start

# Run the full experiment (start server + benchmark + parse)
bash experiments/dsv4_h200_vllm/run_bench.sh

# Reuse an already-running server
SKIP_MANAGE_SERVER=1 bash experiments/dsv4_h200_vllm/run_bench.sh

Results land in experiments/dsv4_h200_vllm/results/<RUN_ID>/.

Configuration

Edit config.env or override via environment variables:

MODEL_PATH=/data/models/DeepSeek-V4-Flash \
PORT=30005 \
SCENARIOS="32 512 256   128 512 256" \
bash experiments/dsv4_h200_vllm/run_bench.sh

Files

File	Purpose
`config.env`	Experiment-level configuration (model, port, venv paths, scenarios)
`start_server.sh`	Start a plain vLLM baseline server for DeepSeek-V4-Flash
`run_bench.sh`	Orchestrator: metadata → server → benchmark → parse
`parse_results.py`	Parse raw JSONL outputs into `results.json` + `report.md`

Platform

This experiment targets platforms/nvidia_h200.env.

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