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 26A5353q, 2026-06-11).

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

LFM2.5-2.6B — Apple Core AI (.aimodel)

LiquidAI's LFM2.5-2.6B converted to Apple's Core AI (the Core ML successor announced at WWDC26), ready to run on iOS 27 / macOS 27. Same conv + full-attention hybrid as the 1.2B, scaled to 30 layers = 22 short-conv mixers + 8 GQA attention layers, hidden 2048, MLP 10 752, 32 q / 8 kv heads, vocab 128 000. No recurrent scan anywhere, so the decode graph is loop-free by construction and rides Apple's coreai-pipelined GPU engine with one fixed-shape conv state and no custom kernels.

This is a reasoning model — the chat template ends the generation prompt with an open <think>, and generations spend their first few hundred tokens thinking. Budget max-tokens accordingly; a 200-token cap regularly ends mid-thought.

Requires iOS 27 / macOS 27 (Core AI ships with the OS). Conversion code, gates and knowledge base: coreai-model-zoo.

Bundles

M4 Max, macOS 27.0 (26A5378n), Xcode 27.0 (27A5218g), coreai-torch 0.4.1, llm-benchmark -p 128 -g 256 -n 3, COREAI_CHUNK_THRESHOLD=1. The gate compares the exported bundle's greedy decode token-for-token against the fp32 eager oracle; transcripts are in the zoo card directory.

No iPhone numbers are published here because none were measured.

int8hu is the quality ship. The head is 128 000 × 2048 = 262 M parameters, so leaving it fp16 costs 524 MB of reads per token; untying and quantizing it to int8 buys +8.6 % decode over plain int8lin (the 1.2B saw +9.3 % for the same reason). The bundle gets bigger — 3.2 → 3.4 GB — because untying stores an fp16 embedding and an int8 head instead of one shared table. That is the trade working, not a regression. int8lin is not published: slower than int8hu and only 0.2 GB smaller, so it has no case of its own.

int4lin did not hit the int4 quality cliff, which is worth saying because this family usually does. Beyond the 16/16 gate, four long greedy generations were read in full — an algorithmic explanation, iterative Fibonacci with complexity analysis, a clock-arithmetic word problem and a Japanese instruction — with grammar intact, arithmetic correct and code correct. Four prompts are not a benchmark: read this as no cliff observed, not int4 is free.

If you are sizing for a phone, note that 524 MB of the 2.0 GB int4lin bundle is the still-fp16 embedding. The remaining size lever on this model is the embedding, not the layers.

Run it

git clone https://github.com/apple/coreai-models   # + the zoo's engine patches, see below
swift build -c release --product llm-runner

COREAI_CHUNK_THRESHOLD=1 .build/release/llm-runner \
  --model gpu-pipelined/lfm2_5_2_6b_decode_int8hu_block32_sym \
  --prompt "Explain why a hash table lookup is O(1) on average but O(n) in the worst case." \
  --max-tokens 512 --sampling-strategy greedy \
  --inference-engine-variant coreai-pipelined --warmup off

--warmup off matters: default warmup submits a synthetic 256-token prefill, and these bundles are static-S=1, so it fails with a shape-substitution error before generating anything. The coreai-pipelined-extra-states patch (which carries the conv state) is in the zoo under apps/.

Converting this family yourself

Two config traps in this checkpoint generation, both silent — nothing raises, and a bundle built without the fix looks like it worked:

1. RoPE theta moved. These are transformers-v5-era configs carrying rope_parameters: {rope_theta: 1e7} instead of a flat rope_theta. Read only the flat key and you fall back to the 1.2B's 1e6 and mis-rotate every position, with no error.
2. The tokenizer class does not exist yet. tokenizer_config.json declares tokenizer_class: "TokenizersBackend", which a transformers-4.x AutoTokenizer cannot resolve. The chat template also lives in its own chat_template.jinja in this era, so the obvious workaround ships a bundle with no template. Load tokenizer.json directly and carry the template across.

Both are handled in conversion/export_lfm2_decode_pipelined.py.

License

LFM Open License v1.0, carried from LiquidAI/LFM2.5-2.6B (revision ab00687315bc1298e9d54e9c4b611dde9867ccc2). Not affiliated with Apple or LiquidAI.

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.