MLX Local AI — Apple's ML Framework Powers Your Entire Fleet

Everything in this fleet runs on Apple's MLX framework. LLM inference, image generation, speech-to-text, embeddings — all MLX-native, all optimized for Apple Silicon's unified memory architecture.

The MLX stack

One router. One framework. Four modalities. All local.

Setup

pip install ollama-herd    # PyPI: https://pypi.org/project/ollama-herd/
herd                       # start the router (port 11435)
herd-node                  # run on each device — finds the router automatically

# Install image generation backends
uv tool install mflux           # Flux models (~7s at 512px)
uv tool install diffusionkit    # Stable Diffusion 3/3.5

All tools leverage MLX for Metal-accelerated inference on Apple Silicon's GPU cores.

LLM inference via MLX

Ollama runs models using MLX on Apple Silicon. Unified memory means the entire model stays in one address space — no PCIe bottleneck.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=[{"role": "user", "content": "Explain MLX unified memory"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Image generation via MLX

Both mflux and DiffusionKit are pure MLX implementations — no PyTorch, no CUDA.

# Flux via mflux (fastest)
curl -o flux.png http://localhost:11435/api/generate-image \
  -H "Content-Type: application/json" \
  -d '{"model": "z-image-turbo", "prompt": "a neural network visualization", "width": 1024, "height": 1024}'

# Stable Diffusion 3 via DiffusionKit
curl -o sd3.png http://localhost:11435/api/generate-image \
  -H "Content-Type: application/json" \
  -d '{"model": "sd3-medium", "prompt": "a circuit board landscape", "width": 1024, "height": 1024, "steps": 20}'

Speech-to-text via MLX

Qwen3-ASR transcribes audio using MLX acceleration.

curl http://localhost:11435/api/transcribe \
  -F "file=@meeting.wav" \
  -F "model=qwen3-asr"

Embeddings via MLX

Ollama embedding models run on the MLX backend.

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Apple MLX framework for machine learning"}'

Why MLX matters for local AI

• Unified memory — model weights, activations, and KV cache share one memory pool. No CPU-GPU transfer overhead.
• Metal acceleration — MLX compiles to Metal shaders that run on Apple Silicon GPU cores (up to 80 on M3/M4 Ultra).
• Lazy evaluation — MLX only computes what's needed, reducing memory pressure.
• Dynamic shapes — no recompilation when input sizes change (unlike some CUDA frameworks).
• Apple-maintained — MLX is developed by Apple's ML research team, optimized for every chip generation.

Fleet performance on Apple Silicon

Monitor your MLX fleet

# Fleet overview
curl -s http://localhost:11435/fleet/status | python3 -m json.tool

# Model recommendations based on your hardware
curl -s http://localhost:11435/dashboard/api/recommendations | python3 -m json.tool

# Health checks
curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool

Dashboard at http://localhost:11435/dashboard — see every node, every model, every queue in real time.

Full documentation

• Agent Setup Guide — all 4 model types
• Image Generation Guide — 3 backends
• API Reference

Contribute

Ollama Herd is open source (MIT) and built on the MLX ecosystem. We welcome contributions:

• Star on GitHub — helps others discover the project
• Open an issue — bug reports, feature requests, questions
• AI agents welcome — CLAUDE.md provides full architectural context. Fork, branch, PR.
• 444 tests, async Python, runs in under 40 seconds. Hard to break things.

Guardrails

• No automatic downloads — all model pulls require explicit user confirmation.
• Model deletion requires explicit user confirmation.
• All requests stay local — no data leaves your network.
• Never delete or modify files in ~/.fleet-manager/.