MLX — Apple Silicon ML Framework

What it is

MLX is an array framework for machine learning on Apple silicon, developed by Apple machine learning research. It provides a NumPy-like Python API with composable function transformations (autodiff, vectorization, optimization), lazy computation, dynamic graph construction, and a unified memory model where no manual data transfers between CPU and GPU are needed.

MLX supports Python, C++, C, and Swift, making it the go-to framework for training and inference on Mac hardware (M1/M2/M3/M4).

How it saves time or tokens

MLX takes advantage of Apple silicon's unified memory architecture, eliminating the CPU-to-GPU data transfer overhead that plagues CUDA-based frameworks on Macs. Operations are evaluated lazily, meaning computations run only when results are needed, reducing unnecessary work. The mlx-lm companion package provides turnkey LLM inference with community-quantized models, letting you run large language models locally on a Mac in minutes.

How to use

1. Install MLX:

pip install mlx

1. Run a quick GPU computation:

import mlx.core as mx

a = mx.random.normal((512, 512))
b = mx.random.normal((512, 512))
c = a @ b
mx.eval(c)
print(f'Result shape: {c.shape}')

1. Run LLM inference with mlx-lm:

pip install mlx-lm
mlx_lm.generate \
  --model mlx-community/Llama-3.2-3B-Instruct-4bit \
  --prompt 'Explain transformers in 3 sentences'

Example

Training a simple neural network with MLX:

import mlx.core as mx
import mlx.nn as nn
import mlx.optimizers as optim

class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 10)

    def __call__(self, x):
        x = nn.relu(self.fc1(x))
        return self.fc2(x)

model = MLP()
optimizer = optim.Adam(learning_rate=1e-3)

def loss_fn(model, x, y):
    return nn.losses.cross_entropy(
        model(x), y
    ).mean()

loss_and_grad = nn.value_and_grad(model, loss_fn)

for batch in data_loader:
    loss, grads = loss_and_grad(model, batch['x'], batch['y'])
    optimizer.update(model, grads)
    mx.eval(model.parameters(), optimizer.state)

Related on TokRepo

• Local LLM tools — Browse local LLM tools and frameworks on TokRepo.
• AI tools for coding — Developer tools for ML and AI.

Common pitfalls

• MLX only runs efficiently on Apple silicon. On Intel Macs or Linux, use PyTorch with CUDA instead.
• Forgetting to call mx.eval() means computations are deferred and never executed. MLX uses lazy evaluation; results are only computed when explicitly evaluated.
• Community-quantized models on Hugging Face vary in quality. Stick to the mlx-community organization for well-tested quantizations.