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).

Qwen3-VL 4B — Core AI (.aimodel)

Qwen/Qwen3-VL-4B-Instruct converted to Apple Core AI (.aimodel, iOS 27 / macOS 27): image+text → text fully on the GPU via Apple's coreai-pipelined engine, zero custom kernels. The 4B sibling of the Qwen3-VL 2B port — it drops onto the same recipe with zero code changes (the model overlay and exporter are fully config-driven).

Part of the CoreAI-Model-Zoo; full card with the conversion design: zoo/qwen3-vl.md.

Use it

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

let caption = try await CoreAI.caption(imageAt: url, options: .model("qwen3-vl-4b"))

Every op, one shape — Cookbook.

▶️ Run it (source) — the VLChat runner (GUI + CLI, one app for every vision-language model in the catalog):

git clone https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/VLChat/VLChat.xcodeproj
# → Run, then pick "Qwen3-VL 4B" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/VLChat
swift run vlchat-cli --model qwen3-vl-4b --image sample.jpg --prompt "What is in this image?"

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

import CoreAIKit
import FoundationModels

let vlm = try await KitVisionModel(catalog: "qwen3-vl-4b")
let session = LanguageModelSession(model: vlm)
let image = try ImageFile.load(imageURL)  // any image file → CGImage + EXIF orientation
let reply = try await session.respond(to: Prompt {
    prompt
    Attachment(image.cgImage, orientation: image.orientation)
})
// reply.content: the answer about the image, generated fully on-device

The take-home is Examples/VLChat/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 KitVisionModel(catalog:) behind a LanguageModelSession. Multi-turn about the same image? Hold the LanguageModelSession and call respond(to:) per turn. The photo picker / file chooser is your app's own chrome — ImageFile.load (kit API) turns any image file into model input.

Integration checklist

• SPM: https://github.com/john-rocky/coreai-kit → product CoreAIKit
• Info.plist: NSPhotoLibraryUsageDescription — only if you use PhotosPicker
• Entitlements (iOS): com.apple.developer.kernel.increased-memory-limit
• First run downloads the model — 5.9 GB (Mac) / 5.9 GB (iPhone) — then it loads from the local cache (Application Support; progress via the downloadProgress callback)
• Measure in Release — Debug is ~3× slower on per-token host work

Measured

Decode is bandwidth-bound: the 4.7 GB int8hu decoder reads ~4.7 GB/token, so it runs at roughly half the 2B's rate. On iPhone the read is heavy enough to thermally throttle — ~14 tok/s from a cool start, settling to ~8.5 under sustained decode. Device cold load 52.7 s (on-device GPU specialization, no AOT), warm 8–9 s; needs the increased-memory entitlement (4.7 GB class).

Files

How it works (short version)

The text-only pipelined engine carries the VLM through an id-space trick — no engine code changes beyond the published static-inputs patch:

• the vision encoder runs once per image; its embeddings ride 4 static graph inputs (rewritable owned MTLBuffers),
• the prompt's <|image_pad|> ids become extension ids vocab + slot; the graph selects text-table vs image-embed rows per token and applies the three DeepStack adds the same way,
• interleaved M-RoPE is derived in-graph from (ids, position) alone — image tokens self-locate, text tokens use a host-set shift; with zero embeds the same bundle is a plain Qwen3 text LLM.

Numerics are gated the zoo way: fp32-HF oracle → torch ladder (position formula exact vs get_rope_index, 36/36 layers) → .aimodel GPU → engine ≡ python 24/24 → device 24/24.

Run it

See the zoo's apps/CoreAIChat (iOS) Qwen3-VL mode and the run contract (S=1 prefill, COREAI_CHUNK_THRESHOLD=1, never engine.warmup()) in knowledge/pipelined-engine.md.

Conversion is reproducible from the zoo: conversion/export_qwen3_vl_pipelined.py int8hu --hf-id Qwen/Qwen3-VL-4B-Instruct.

License

Apache-2.0 (inherited from Qwen3-VL-4B-Instruct). Conversion code BSD-3-Clause (zoo repo).

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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.