Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta, 2026-06).

This model has no row on DeviceMark, the on-device LLM leaderboard.

Apple Core AI (.aimodel) conversion of LiquidAI/LFM2.5-8B-A1B: a conv + full-attention MoE hybrid decoder (24 layers = 18 short-conv mixers + 6 GQA attention; hidden 2048, vocab 128k; first 2 layers dense, the rest 32-expert top-4 sparse MoE). 8.3B total / ~1.5B active per token.

Part of the community Core AI model zoo: https://github.com/john-rocky/coreai-model-zoo (full card: zoo/lfm2.5-8b-a1b-moe.md).

⚡ One line — run the kit's task op on this model (import CoreAIOps; no session, no model plumbing, downloads on first use):

Every op, one shape — Cookbook.

▶️ Run it (source) — the ChatDemo runner (GUI + CLI, one app for every chat model in the catalog):

💻 Build with it — complete; the glue is kit API, copy-paste runs:

When Apple's FoundationModels built-in model isn't enough, keep your session code and swap the model — one line. CoreAIKit's KitLanguageModel plugs this bundle into the same system LanguageModelSession; your Tools, @Generable types and transcripts work unchanged, and capabilities (tool calling, guided generation) auto-detect per model.

The take-home is Examples/ChatDemo/Sources/QuickStart.swift — this exact code as one typed function, no UI; the CLI is an argument shell over it, and the GUI drives the same ChatSession across turns for its transcript. Multi-turn? Hold the ChatSession and call respond(to:) per turn — it keeps the conversation history; streamResponse(to:) yields tokens as they decode.

MoE decode normally reads all 32 experts' weights every token via the GatherMM composite even though only the top-4 are routed — bandwidth-bound at 39 tok/s. This bundle uses a custom coreai_torch.TorchMetalKernel that takes the routed indices as a kernel input and reads only the 4 routed experts' weight slabs → 3.6× faster (141 tok/s) at the same active-param bandwidth.

Honest bottom line. The sym8 (symmetric-linear int8) Mac bundle is both 3.6× faster AND clean — at the fp16 ceiling, matching the shipped int8-linear quality. The kernel itself is bit-exact; quality is purely the expert quantization scheme. An int4 bundle (4.7 GB) was validated to run on the iPhone 17 Pro (~32 tok/s, the first MoE on the phone) — but the iPhone needs int4 for size and non-QAT int4 is a hard quality wall (two independent 4-bit schemes both land at ~12 introduced flips/41 with large margins; clean int4 would need QAT weights LiquidAI doesn't ship). So only the clean Mac bundle is shipped; rebuild the int4 variant locally if you want the on-device version. On a bare prompt the base model itself greedy-degenerates into repetition (present in fp16 too) — use the chat template + sampling.

The decode graph's input_ids is static [1,1]; prefill runs as S=1 pipelined steps. Convert your own with conversion/export_lfm2_moe_metal_decode_pipelined.py (sym8 = clean Mac; int4km = iPhone-compact, not shipped).

LFM Open License v1.0 (upstream LiquidAI license, shipped as LICENSE). Conversion/kernel: community.

More models in this format: Core AI Model Zoo — 75 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.