Run MLX on Apple Silicon in the Cloud: Training and Inference with Metal

MLX is Apple's open-source machine learning framework, designed from the ground up for Apple Silicon. Unlike PyTorch or TensorFlow, which bolt on Metal support as an afterthought, MLX treats the unified memory architecture as a first-class citizen. Arrays live in shared memory and can be operated on by CPU, GPU, or Neural Engine without copying. The result is a NumPy-like API that trains and runs models with remarkably low overhead on M-series chips.

Why Apple Silicon Changes the Game for MLX

MLX's lazy evaluation and unified memory model mean that on an M4 Pro with 48 GB of RAM, you can fine-tune a 7B-parameter model without ever hitting a memory wall. There's no GPU VRAM to worry about — the entire 48 GB is available to both compute and data. Metal acceleration handles matrix operations on the 20-core GPU, while the Neural Engine picks up quantized inference workloads. Training a LoRA adapter on Llama 3 takes minutes, not hours.

Deploying MLX on Macyou

Head to the Macyou Catalog and find the MLX stack. One click deploys a Mac Mini M4 Pro with Python 3.12, MLX, and mlx-lm pre-installed. SSH in and start training immediately — no driver installation, no CUDA toolkit, no environment debugging.

$ ssh root@YOUR_IP
$ python -c "import mlx.core as mx; print(mx.default_device())"
gpu

$ mlx_lm.lora --model mlx-community/Llama-3-8B-4bit \
    --data ./my-dataset --batch-size 4 --num-layers 8
Training... 142 tok/s on M4 Pro GPU

Example Workflow: Fine-Tune and Serve

A typical MLX workflow on Macyou looks like this: pull a pre-converted model from the mlx-community on Hugging Face, fine-tune it with LoRA on your custom dataset, fuse the adapter weights, and serve the result with mlx-lm's built-in server. The server exposes an OpenAI-compatible API, so your existing application code works without changes. The entire cycle — from raw data to production endpoint — happens on a single machine.

Recommended Tier and Pricing

For MLX training workloads, we recommend the Standard tier ($299/mo) with 48 GB RAM, which handles fine-tuning models up to 13B parameters comfortably. For larger models or concurrent training runs, the Advanced tier ($599/mo) with 64 GB gives more headroom. Check the full breakdown on our pricing page.

Ready to train on Apple Silicon? Browse the catalog and deploy MLX in under a minute.