# 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

```bash
# 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:

```bash
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`.
