mlx-local-inference

# MLX Local Inference Stack

Local AI inference on Apple Silicon. oMLX handles LLM/VLM with continuous batching. Python libraries handle Embedding/ASR/OCR directly via `uv`.

Architecture

``` ┌─────────────────────────────────────┐ │ oMLX (localhost:8000/v1) │ │ - LLM (Qwen3.5-35B, etc.) │ │ - VLM (vision-language models) │ │ - Continuous batching + SSD cache │ └─────────────────────────────────────┘

┌─────────────────────────────────────┐ │ Python Libraries (via uv run) │ │ - mlx-lm: Embedding │ │ - mlx-vlm: OCR (PaddleOCR-VL) │ │ - mlx-audio: ASR (Qwen3-ASR) │ └─────────────────────────────────────┘ ```

Models

| Capability | Implementation | Model | Size | |-----------|---------------|-------|------| | 💬 LLM | oMLX API | `Qwen3.5-35B-A3B-4bit` | ~20 GB | | 👁️ VLM | oMLX API | Any mlx-vlm model | varies | | 📐 Embed | mlx-lm (uv) | `Qwen3-Embedding-0.6B-4bit-DWQ` | ~1 GB | | 🎤 ASR | mlx-audio (uv) | `Qwen3-ASR-1.7B-8bit` | ~1.5 GB | | 👁️ OCR | mlx-vlm (uv) | `PaddleOCR-VL-1.5-6bit` | ~3.3 GB |

Usage

LLM / Vision-Language (via oMLX API)

```python from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="local")

# Text generation resp = client.chat.completions.create( model="Qwen3.5-35B-A3B-4bit", messages=[{"role": "user", "content": "Hello"}] ) print(resp.choices[0].message.content) ```

---

Embeddings (via mlx-lm + uv)

```bash uv run --with mlx-lm python -c " from mlx_lm import load model, tokenizer = load('~/models/Qwen3-Embedding-0.6B-4bit-DWQ') text = 'text to embed' inputs = tokenizer(text, return_tensors='np') embeddings = model(**inputs).last_hidden_state.mean(axis=1) print(embeddings.shape) " ```

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ASR — Speech-to-Text (via mlx-audio + uv)

> Important: Must run with `--python 3.11` to avoid OpenMP threading issues (`SIGSEGV`).

```bash uv run --python 3.11 --with mlx-audio python -m mlx_audio.stt.generate \ --model ~/models/Qwen3-ASR-1.7B-8bit \ --audio "audio.wav" \ --output-path /tmp/asr_result \ --format txt \ --language zh \ --verbose ```

---

OCR (via mlx-vlm + uv)

> Important: The `generate` function parameter order must be `(model, processor, prompt, image)`.

```bash cat << 'PY_EOF' > run_ocr.py import os from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template

model_path = os.path.expanduser("~/models/PaddleOCR-VL-1.5-6bit") model, processor = load(model_path) prompt = apply_chat_template(processor, config=model.config, prompt="OCR:", num_images=1)

output = generate(model, processor, prompt, "document.jpg", max_tokens=512, temp=0.0) print(output.text) PY_EOF

uv run --python 3.11 --with mlx-vlm python run_ocr.py ```

---

Service Management (oMLX only)

```bash # Check running models curl http://localhost:8000/v1/models

# Restart oMLX launchctl kickstart -k gui/$(id -u)/com.omlx-server ```

Model Storage Strategy

All models stored in `~/models/` using oMLX-compatible structure:

``` ~/models/ ├── Qwen3-Embedding-0.6B-4bit-DWQ/ ├── Qwen3-ASR-1.7B-8bit/ ├── PaddleOCR-VL-1.5-6bit/ └── Qwen3.5-35B-A3B-4bit/ ```

Requirements

• Apple Silicon Mac (M1/M2/M3/M4)
• `uv` installed (`curl -LsSf https://astral.sh/uv/install.sh | sh`)