---
license: apache-2.0
base_model: Qwen/Qwen3-VL-2B-Instruct
tags:
- coreai
- apple
- ios
- macos
- on-device
- vision-language
- vlm
- qwen3-vl
---

# Qwen3-VL 2B — Core AI (`.aimodel`)

**The first vision-language model on Apple's Core AI framework** (iOS 27 /
macOS 27): `Qwen/Qwen3-VL-2B-Instruct` converted to `.aimodel`, running
image+text → text fully on the GPU via Apple's `coreai-pipelined` engine —
zero custom kernels.

Part of the [CoreAI-Model-Zoo](https://github.com/john-rocky/coreai-model-zoo);
full card with the conversion design:
[zoo/qwen3-vl.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/qwen3-vl.md).

![CoreAIChat Qwen3-VL demo](demo.gif)

<!-- gen-cards:use-it begin id=qwen3-vl-2b (managed by scripts/gen-cards — edit cards.json / QuickStart.swift, not this block) -->
![Qwen3-VL 2B demo](https://huggingface.co/mlboydaisuke/Qwen3-VL-2B-CoreAI/resolve/main/demo.gif)
*Qwen3-VL 2B on iPhone 17 Pro — in the zoo's CoreAIChat app, real speed.*

## Use it

▶️ **Run it (source)** — the [VLChat runner](https://github.com/john-rocky/coreai-kit/tree/main/Examples/VLChat)
(GUI + CLI, one app for every vision-language model in the catalog):

```bash
git clone https://github.com/john-rocky/coreai-kit
open coreai-kit/Examples/VLChat/VLChat.xcodeproj
# → Run, then pick "Qwen3-VL 2B" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/VLChat
swift run vlchat-cli --model qwen3-vl-2b --image sample.jpg --prompt "What is in this image?"
```

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

```swift
import CoreAIKit
import FoundationModels

let vlm = try await KitVisionModel(catalog: "qwen3-vl-2b")
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`](https://github.com/john-rocky/coreai-kit/blob/main/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 — 3.3 GB (Mac) / 3.3 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
<!-- gen-cards:use-it end -->

## Measured

| platform | prefill tok/s | decode tok/s | numerics |
|---|---:|---:|---|
| M4 Max (macOS 27 beta) | **191.0** | **187.6** | full multimodal oracle gates vs fp32-HF PASS |
| iPhone 17 Pro (iOS 27 beta, settled) | **33.9** | **33.3** | text + image prompts 24/24 × 8 runs, token-identical to Mac (~92% of the naive BW ceiling) |

Vision encode: ~60-80 ms/image (Mac GPU). Device cold load 12.3 s
(on-device GPU specialization, no AOT), warm 0.6–5 s. The 2.3 GB decoder
wants the increased-memory entitlement on iPhone.

## Files

| path | what | size |
|---|---|---:|
| `gpu-pipelined/qwen3_vl_2b_instruct_decode_int8hu_s1/` | text decoder LanguageBundle (SHIP: int8 per-block-32 body + untied absmax int8 head; tokenizer + metadata included) | 2.3 GB |
| `gpu-pipelined/qwen3_vl_2b_instruct_vision/` | fixed-grid vision encoder (448×448 → 196 tokens + DeepStack), fp16 | 0.77 GB |
| `gpu-pipelined/qwen3_vl_2b_instruct_decode_int8lin_s1/` | decoder alt: tied fp16 head (slower, smaller-RAM-spike option) | 2.0 GB |

## 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](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps):

- the vision encoder runs once per image; its embeddings ride **4 static
  graph inputs** (rewritable owned `MTLBuffer`s, ~3 MB),
- 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`, 28/28 layers) → `.aimodel` GPU gates →
engine ≡ python 24/24 → device 24/24.

## Run it

The zoo's `apps/CoreAIChat` (iOS) has a Qwen3-VL mode with a photo picker
and downloads this repo in-app. For the run contract (S=1 prefill,
`COREAI_CHUNK_THRESHOLD=1`, never `engine.warmup()`), see
[knowledge/pipelined-engine.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/pipelined-engine.md).

Conversion is reproducible from the zoo:
`conversion/export_qwen3_vl_pipelined.py int8hu`.

## License

Apache-2.0 (inherited from Qwen3-VL-2B-Instruct). Conversion code BSD-3-Clause
(zoo repo).