Gpu Perf Tune

gpu-perf-tune

GPU performance engineering for agents and MCP clients, with an inference-focused skills package and reusable cluster utilities. The project ships 32 task-oriented Agent Skills, a bundled MCP server, safety guards, workload proof schemas, and local validation tools. Together they cover first estimates, benchmark sweeps, kernel profiling, speed-of-light analysis, optimization, and evidence-backed reports.

The work comes from GPU fleet performance practice. Every result stays a candidate until the workload, baseline, hardware, precision, parallelism, and engine version are recorded and the result survives a skeptical check.

Names and boundaries

Claude Code is one supported client, not the project boundary. Client support depends on the surface being installed.

Client support

The SKILL.md sources follow the open Agent Skills standard. This repository maintains and tests the Claude Code and Codex paths. The MCP protocol remains client-neutral. Configuration helpers for other clients are available, but they are not part of the release bar.

What it covers

1. Estimate, benchmark, and sweep: inference-performance-hints for rough performance bounds, inference-perf-bench for load sweeps, inference-tune-sweep for engine knobs, inference-model-eval for quality gates, and perf-baseline-record with perf-baseline-diff for regressions.
2. Profile: inference-workload-profile, inference-kernel-profile for nsys, inference-kernel-ncu-profile for per-kernel roofline work, inference-dcgm-correlate, analyze-zymtrace-workload, inference-graph-diff, and mirage-graph-coverage.
3. Optimize: inference-model-optimize, inference-quantize-calibrate, the speculative decode train, tune, and service skills, inference-decode-step-budget, inference-capacity-sizing, and inference-known-good-config.
4. Report and track: inference-perf-tune-report, inference-perf-synthesize, inference-fleet-leaderboard, inference-value-ledger, evidence-bundle-init, and the anchored Prometheus and zymtrace query skills.

The documented command-line path remains available when an external observability MCP server is absent.

Quickstart

Inspect the project without a GPU

git clone https://github.com/cfregly/gpu-perf-tune.git
cd gpu-perf-tune
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements/check.txt
make demo
make check
make workload-proof-check

make demo prints the skill and MCP tool surface. A real performance run needs the bundled server, the target workload, and suitable GPU hardware.

Codex: skills and MCP

From a clone of this repository:

make install-skills CLIENT=codex
make install-mcp CLIENT=codex
codex mcp get profile_and_optimize

The registration check should report enabled: true. Run make smoke-mcp-runtime for a live server handshake, then restart Codex after installation. Codex discovers the linked skills under ~/.agents/skills. Use /skills, invoke a skill with $skill-name, or describe the task and let Codex select one. The MCP configuration is shared by Codex CLI, the IDE extension, and the desktop app. See the official Codex Skills and MCP references for the client-side contracts.

Claude Code: skills, MCP, and optional provenance enforcement

claude plugin marketplace add cfregly/gpu-perf-tune
claude plugin install --scope user \
  profile-and-optimize@profile-and-optimize-plugins

# Install the bundled MCP server inside the current plugin cache entry.
# Add --full when you need the PDF report dependencies.
bash "$(ls -dt ~/.claude/plugins/cache/profile-and-optimize-plugins/profile-and-optimize/*/server/install.sh | head -1)"

claude mcp get plugin:profile-and-optimize:profile_and_optimize

The health check should report Status: ✔ Connected. Restart Claude Code. Invoke a skill such as /inference-perf-bench, or describe the task and let Claude Code select a matching skill. The plugin installs its provenance hook, but the hook remains inactive until PROVENANCE_COMMIT_GATE=ask or PROVENANCE_COMMIT_GATE=deny is present in the Claude hook environment. Install jq on PATH before enabling either mode.

Other clients: best-effort helpers

The repository keeps configuration helpers for Cursor, Gemini CLI, and Google Antigravity. They are useful starting points, but changes to those clients do not block a release. The full command list and generic stdio form live in the MCP installation reference.

make install-skills CLIENT=cursor
make install-mcp CLIENT=cursor

Upgrading

Read docs/UPGRADING.md before changing release lines. It lists the client refresh commands and required installer, AI tuning, MLPerf rules, and MCP acknowledgement changes.

Value bar

Every benchmark result, optimization claim, and generated report starts as a candidate. It must be adversarially-confirmed to add value before it ships. The workload is named, the baseline is fair, a skeptic has tried to break the finding, and the receipt maps to lower cost, faster runtime, higher throughput, better reliability, or a clearer operator action.

Workload proof contract

docs/workload-proof-packet.md defines the GPU inference packet shape for neocloud buyers and workflow handoffs. make workload-proof-check validates checked-in packets for completeness and local handoff metadata.

Repository layout

Methodology

The skills enforce DRAFT and VERDICT labels, full performance context, asset validation, and a clear next action. Read docs/METHODOLOGY.md for the shared rules. The performance hints adaptation adds estimate-first triage, with a synthetic example.

Optional integrations

A workflow system can consume workflow_handoff blocks when GPU workload evidence needs to attach to a broader customer record. ProofPlane is one possible consumer. It does not change this project's local contract, validation gates, or runtime dependencies.

Development

• Add a skill from plugins/profile-and-optimize/templates/skill/SKILL.md.
• Add an MCP verb under plugins/profile-and-optimize/server/ and update mcp_surface.py.
• Read CONTRIBUTING.md before opening a pull request.
• Run make help for the local command reference.

Limitations

The project helps agents measure and report. It does not tune a cluster by itself. Every number depends on the workload and runtime context. Datasheet speed-of-light ceilings are upper bounds, not promises. External systems such as Grafana, Prometheus, GitHub, and zymtrace still need operator credentials and their own client configuration.

License

Project-authored material is MIT licensed. Third-party adaptations retain their own terms and attribution in THIRD_PARTY_NOTICES.md and LICENSES/.