On-device models

FoundationModelsModel runs Apple's on-device model, and optionally Private Cloud Compute, through the exact LanguageModel protocol as every cloud provider. No network, no key, no data leaving the device.

let local = FoundationModelsModel()

let result = try await generateText(
  model: local,
  prompt: "One-line haiku about rain."
)

The web can't do this. If you're deciding whether an AI feature belongs in your native app or your web app, this is the argument.

Availability and fallback

On-device availability depends on the device, OS, and Apple Intelligence settings. Check it and fall back in one line:

let model: any LanguageModel = FoundationModelsModel.isAvailable
  ? FoundationModelsModel()
  : AnthropicModel("claude-sonnet-5")

Everything downstream (tools, streaming, structured output, chat sessions) is identical for both branches.

Private Cloud Compute

Requests can target Apple's Private Cloud Compute instead of the on-device model:

let pcc = FoundationModelsModel.privateCloudCompute()

What works

• generateText and streamText, including multi-turn history: the pack maintains a Foundation Models Transcript across calls.
• Tools: calls surface through the same loop as every other provider.
• generateObject: guided generation maps your JSON Schema onto the framework's constrained decoding.
• Chat sessions: ChatSession(model: FoundationModelsModel()) gives a fully offline chat UI.

Error taxonomy

Failures arrive as typed errors where the framework provides them (guardrail violations, context overflows, unavailable model assets), with clear messages for the cases Apple reports only as generic NSErrors. Unentitled processes on some OS builds cannot run on-device inference at all; the error message says exactly that instead of a cryptic code.