gaelic-ghost Choose Apple Model Runtime
Route By Artifact And Constraint
| Need | Start with |
|---|---|
| Host a downloaded model locally for an API client, agent framework, or dev tool | LM Studio local server; choose native /api/v1 model-management APIs or OpenAI-compatible /v1 inference APIs deliberately |
Author .aimodel packages with editable Python primitives and Swift runtime utilities |
Choose Core AI, then hand off to the coreai-models working-with-coreai and model-authoring skills |
Lower torch.export.ExportedProgram into Core AI IR |
coreai-torch |
| Quantize, palettize, or prune Core AI models | Choose Core AI, then hand off to Apple's model-compression-exploration skill and coreai-optimization |
| Convert and deploy established Core ML model packages | coremltools plus Core ML |
| Train or run tensor programs natively on Apple silicon | MLX |
| Integrate MLX models in Swift | MLX Swift |
| Fine-tune or serve supported language models with MLX | MLX LM |
Use one ExecuTorch .pte pipeline with Apple acceleration |
Compare the ExecuTorch Core ML backend and experimental MLX delegate |
| Use Apple's system on-device language model without shipping weights | Foundation Models framework; use Python Apple FM SDK for supported Python access |
Decision Workflow
- Decide whether the need is a local inference server for a client or an app-packaged runtime/artifact. Use LM Studio for the former; identify the source artifact for the latter.
- Identify the deployment API: Python research, Swift app, ExecuTorch C++/mobile, or Foundation Models.
- Consult the dated maturity and availability matrix in
references/apple-model-tooling.md, then confirm OS, Xcode, SDK, device, architecture, operator, dynamic-shape, state/cache, and precision requirements against the current official source. - Select the shortest supported conversion path. Do not round-trip through formats merely because converters exist.
- Prototype one representative subgraph and one stateful generation step before converting the full model.
- Evaluate numerical/behavioral parity on the exact packaged artifact.
- Benchmark on the target device with
benchmark-model-runtime. - Return the selected runtime, maturity class, source revision/date checked, unmet availability gates, and implementation owner. Hand Core AI authoring/compression to Apple's named skills and app-facing Swift/Xcode work to
apple-dev-skills.
Important Distinctions
- Core AI and Core ML are related Apple deployment surfaces but are not interchangeable artifact formats or APIs.
- MLX is a general Apple-silicon array framework; MLX LM and MLX Swift are distinct higher-level/use-language surfaces.
- ExecuTorch's MLX delegate is marked experimental and under active development upstream. Treat support as revision-specific and compare it separately with the Core ML backend.
- Foundation Models uses Apple's system model and availability contract; it is not a route for packaging arbitrary user-supplied weights.
- LM Studio is a local model server and control plane, not a Core AI, Core ML, MLX, or Foundation Models artifact/runtime. Its endpoint compatibility does not prove model tool use or structured-output behavior.
- Apple research repositories vary from reusable frameworks to benchmark or paper-reproduction code. Classify the repository before recommending it as infrastructure.
References
Read references/apple-model-tooling.md for the official source map and verification checklist.