vllm-optimize

name	vllm-optimize
description	Run vLLM benchmark and profiling workflow in containerized environments. Provides automated model loading, concurrency sweep, torch profiling, and GPU kernel analysis. Works with AMD MI355X/MI300X and NVIDIA GPUs.
compatibility	claude-code, opencode
metadata	{"workflow":"vllm-benchmark","audience":"performance-engineers","distribution":"standalone-skill-repo"}

vLLM Optimize

Automated vLLM inference benchmark and profiling workflow for containerized environments.

Quick Start

Run the workflow with a model name:

use vllm-optimize skill for Qwen/Qwen3.5-35B-A3B

The skill will automatically:

1. Start vLLM server with the specified model
2. Run benchmark at various concurrency levels
3. Generate profiling traces
4. Analyze GPU kernel performance

First-turn Latency Rule

• Do not read any other file before the first visible reply
• Send one short kickoff status update explaining the workflow
• Ask the first grouped setup form with options

Guided Setup Flow

1. Start with one short high-level question round:

   • Run plan (smoke test vs full sweep)
   • Output (where to save results)
   • GPUs (which GPUs to use)

2. After Round 1 answers, read INTAKE.md for deeper config

3. Read RUNTIME.md for execution bootstrap

4. Summarize the final plan and get confirmation before executing

5. After confirmation, start execution following phase docs

Modes

• full: benchmark + profiling + analysis
• benchmark: benchmark only (faster)
• profile: profiling only (requires server running)

Files to Read

1. Before Round 1: no extra file reads required
2. After Round 1 answers: INTAKE.md
3. Before execution: RUNTIME.md
4. Phase docs: phases/*.md

References

• INTAKE.md - Configuration options
• RUNTIME.md - Execution details
• Phase 1: vLLM Server Setup
• Phase 2: Benchmark Execution
• Phase 3: Profiling
• Phase 4: Analysis