README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-VL-8B-Instruct
tags:
- coreai
- apple
- macos
- on-device
- vision-language
- vlm
- qwen3-vl
---

# Qwen3-VL 8B — Core AI (`.aimodel`)

`Qwen/Qwen3-VL-8B-Instruct` converted to Apple **Core AI** (`.aimodel`,
macOS 27): image+text → text fully on the GPU via Apple's `coreai-pipelined`
engine, zero custom kernels. The 8B sibling of the
[Qwen3-VL 2B](https://huggingface.co/mlboydaisuke/Qwen3-VL-2B-CoreAI) port —
**same recipe**, with one one-line loader change for its **untied** LM head.

> **Mac-only.** The 8.7 GB int8hu decoder exceeds the iPhone increased-memory
> jetsam ceiling (~6.4 GB class). For on-device iPhone use, see the
> [4B](https://huggingface.co/mlboydaisuke/Qwen3-VL-4B-CoreAI) or
> [2B](https://huggingface.co/mlboydaisuke/Qwen3-VL-2B-CoreAI) ports.

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).

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## 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 8B" in the model picker

# agents / headless (macOS):
cd coreai-kit/Examples/VLChat
swift run vlchat-cli --model qwen3-vl-8b --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-8b")
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: none needed (macOS)
- First run downloads the model — 10.5 GB (Mac) — 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) | **54.4** | **54.3** | torch ladder vs fp32-HF incl. untied head + depth-27 ViT (vision cos 1.0001, 36/36 layers cos 1.000, decode 16/16) + engine ≡ python 24/24 on the 211-tok multimodal prompt |

Decode is bandwidth-bound: the 8.7 GB int8hu decoder reads ~8.7 GB/token.
Vision encode runs once per image. Cold GPU specialization ~16.5 s, warm load
a few seconds.

## Files

| path | what | size |
|---|---|---:|
| `gpu-pipelined/qwen3_vl_8b_instruct_decode_int8hu_s1/` | text decoder LanguageBundle (SHIP: int8 per-block-32 body + untied absmax int8 head; tokenizer + metadata included) | 8.7 GB |
| `gpu-pipelined/qwen3_vl_8b_instruct_vision/` | fixed-grid vision encoder (448×448 → 196 tokens + DeepStack), fp16 | 1.1 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),
- 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.

The 8B differs from 2B/4B only in configuration: its LM head is **untied**
(a separate `lm_head.weight`, quantized int8 absmax like the body) and its
ViT is larger (depth 27, hidden 1152) — both absorbed by the config-driven
overlay. Numerics are gated the zoo way: fp32-HF oracle → torch ladder
(36/36 layers) → engine ≡ python 24/24.

## Run it

Conversion is reproducible from the zoo:
`conversion/export_qwen3_vl_pipelined.py int8hu --hf-id Qwen/Qwen3-VL-8B-Instruct`.
For the run contract (S=1 prefill, `COREAI_CHUNK_THRESHOLD=1`), see
[knowledge/pipelined-engine.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/pipelined-engine.md).

## License

Apache-2.0 (inherited from Qwen3-VL-8B-Instruct). Conversion code BSD-3-Clause
(zoo repo).