Arm Migrate

Migrate your LLM workload to Arm — and prove the speedup with real numbers.

Arm-Migrate is an MCP server plus a GitHub Actions benchmark harness. Connect it to any MCP client (Claude Code, Claude Desktop, …) and ask it to migrate an LLM inference workload to Arm64. It will:

1. Analyze the workload (analyze_workload) — scans Dockerfiles, compose files, and dependency lists for migration blockers: x86-pinned base images, CUDA-only stacks, missing quantization, arch-specific wheels.
2. Plan the migration (generate_migration_plan) — emits ready-to-commit artifacts: an arm64 Dockerfile built around llama.cpp with KleidiAI kernels, a CI benchmark workflow, recommended quantization (Q4_0 for KleidiAI's int8mm/dotprod paths), and a migration checklist.
3. Measure (trigger_benchmark / fetch_benchmark_results) — runs a 3-way benchmark matrix on GitHub's free ubuntu-24.04-arm runners: Arm64 + KleidiAI vs Arm64 baseline vs x86 baseline, using llama-bench with repetitions and captured CPU feature flags.
4. Report (generate_report) — turns the raw JSON artifacts into a migration report: prompt-processing and token-generation tokens/sec, deltas, hardware context, and a go/no-go recommendation.

Zero-cost, fully reproducible: everything runs on free public-repo CI. No GPUs, no cloud account, no API keys.

Measured results (Neoverse-N2, free GitHub Arm64 runners)

Qwen2.5-0.5B-Instruct, llama-bench, 5 repetitions, 4 threads. Full reports with stddev and hardware context: Q4_0 · Q8_0.

Comparison	Prompt proc.	Generation
Arm optimized kernels vs naive Arm build (Q4_0)	+131%	+44%
KleidiAI vs default kernels (Q4_0)	~0%	~0%
KleidiAI vs default kernels (Q8_0)	+59%	+15%
Arm64+KleidiAI vs x86 runner (Q8_0)	+269%	+155%

The practical guidance that falls out: Q4_0 is fast on Arm out of the box (mainline repack kernels); Q8_0 leaves large gains on the table unless you build with -DGGML_CPU_KLEIDIAI=ON. Arm64 wins token generation — the axis that dominates chat/agent serving cost — across every silicon draw we measured; the x86 prompt-processing picture depends on whether GitHub's mixed pool hands you AVX-512 (see the variance disclosure in the reports).

Why this matters

Arm64 cloud (Graviton, Axion, Cobalt, Ampere) is routinely the cheapest compute per vCPU, and KleidiAI makes CPU-only LLM inference genuinely usable — but teams don't migrate because they can't predict what their workload gains. Arm-Migrate closes that gap: the agent hands you the migration plan and the measured numbers in one conversation.

Quickstart

npm install && npm run build

Register with your MCP client (Claude Code shown):

claude mcp add arm-migrate -- node <path>/dist/index.js

Then ask: "Analyze this Dockerfile for Arm migration and generate a plan."

To reproduce our benchmark numbers: fork, enable Actions, run the arm-bench workflow (workflow_dispatch) — the report lands in the run's artifacts and job summary.

Repository layout

src/            MCP server (TypeScript)
bench/          benchmark scripts + report generator (no deps)
templates/      generated migration artifacts (Dockerfile.arm64, …)
.github/        the arm-bench harness itself

Hackathon

Built for Arm Create: AI Optimization Challenge 2026 (Track 2 — Cloud AI). See RULES.md for the rules digest and DEVLOG.md for an honest build log.

License

MIT