MLX vs PyTorch: Which Framework Fits Your Project?

In the MLX vs PyTorch choice, pick MLX when your work starts and ends on an Apple silicon Mac: it is Apple's own array framework, built around the memory that the Mac's CPU and GPU share, and its companion package MLX LM runs, quantizes and fine tunes open language models with a couple of commands. Pick PyTorch when your code also has to run on NVIDIA or AMD servers, when you depend on libraries such as Hugging Face Transformers, or when you are following papers and tutorials, because nearly all of them are written for PyTorch first.

Plenty of Mac owners end up using both. PyTorch runs on the Mac GPU through its MPS (Metal Performance Shaders) backend, and the two frameworks can pass tensors to each other without copying in many cases. This guide compares them on hardware, programming model, speed tools, LLM work and setup, using each project's official documentation and release notes as of November 9, 2026. We did not run benchmarks for this guide, and neither project publishes an official head to head comparison.

MLX vs PyTorch at a glance

Versions come from the MLX releases page and the PyTorch 2.15.0 release notes. Requirements come from the MLX install guide and those same release notes.

What MLX is

The MLX README calls it "an array framework for machine learning on Apple silicon." Its core Python API closely follows NumPy, and its higher level mlx.nn and mlx.optimizers packages closely follow PyTorch, so a PyTorch user can read most MLX model code on the first try. There are also C++, C and Swift APIs, which matter if you want to put a model inside a Mac or iPhone app.

Three design choices shape how MLX feels:

• Unified memory. Arrays live in memory that the CPU and GPU share. You do not move an array to a device. You choose the device when you run an operation, and MLX handles the ordering between CPU and GPU work.
• Lazy computation. Operations build a graph, and nothing is computed until you call mx.eval or need a value.
• Composable function transforms. Gradients and vectorization come from transforming functions, in the style of JAX, and changing input shapes does not trigger a slow recompile.

MLX is no longer limited to the Mac. The install guide lists pip install mlx[cuda12] or mlx[cuda13] for NVIDIA GPUs from compute capability 7.5 upward on Linux, and mlx[cpu] for Linux machines without a GPU.

What PyTorch is

PyTorch is the default deep learning framework for research and for most open model tooling. It runs eagerly, so each line executes when you call it, which makes debugging with ordinary Python tools easy. You can then speed code up with torch.compile.

Its hardware reach is the widest of any framework in this comparison. The 2.15 release alone ships CUDA 13.2 as the default wheel with CUDA 13.4 alongside it, ROCm 10.0 wheels for AMD, work on Intel GPUs, and the MPS backend for Apple silicon. On a Mac, the MPS backend sends tensor work to the GPU through Apple's Metal framework.

PyTorch on Apple silicon keeps improving. According to the 2.15 release notes, the MPS backend gained float8_e4m3fn support, a native grouped_mm for mixture of experts models with a decode fast path that the PyTorch team measured at 1.32 to 1.34 times the tokens per second on MoE decode, public torch.mps.Stream APIs, and more native Metal kernels. The same notes list two catches. MPS now requires macOS 15 (Sequoia) or newer, so on macOS 14 torch.backends.mps.is_available() returns False with 2.15. And MPS still lacks float64.

Unified memory vs devices: the core difference

On a Mac, this is the difference you will notice first.

In PyTorch you create a tensor on a device and move it when needed:

import torch

device = "mps" if torch.backends.mps.is_available() else "cpu"
x = torch.randn(1024, 1024, device=device)
w = torch.randn(1024, 1024, device=device)
y = x @ w
print(y.sum().item())

In MLX there is no .to(). Arrays sit in shared memory, and you can pick a device per operation:

import mlx.core as mx

x = mx.random.normal((1024, 1024))
w = mx.random.normal((1024, 1024))
y = mx.matmul(x, w, stream=mx.gpu)
mx.eval(y)
print(y.sum().item())

For training loops, this mostly removes a class of bugs (tensors on the wrong device) and some copy overhead. For loading large models it means MLX can use most of a Mac's memory for weights without staging them through a separate device copy. It does not mean MLX is always faster: speed depends on the kernels each framework has for the operations your model uses.

Writing and training a model

The neural network APIs look alike, but gradients work differently. Here is one training step in each.

# PyTorch
import torch, torch.nn as nn

model = nn.Linear(4, 1)
opt = torch.optim.SGD(model.parameters(), lr=0.1)
x, y = torch.randn(8, 4), torch.randn(8, 1)

loss = ((model(x) - y) ** 2).mean()
opt.zero_grad()
loss.backward()
opt.step()

# MLX
import mlx.core as mx, mlx.nn as nn, mlx.optimizers as optim

model = nn.Linear(4, 1)
opt = optim.SGD(learning_rate=0.1)
x, y = mx.random.normal((8, 4)), mx.random.normal((8, 1))

def loss_fn(model, x, y):
    return mx.mean((model(x) - y) ** 2)

loss_and_grad = nn.value_and_grad(model, loss_fn)
loss, grads = loss_and_grad(model, x, y)
opt.update(model, grads)
mx.eval(model.parameters(), opt.state)

PyTorch records operations and walks them backward when you call .backward(). MLX transforms the loss function into one that returns gradients, the way JAX does. If you already know PyTorch, MLX's layers and optimizers will look familiar, while the gradient step takes an afternoon to get used to. Our PyTorch vs JAX guide explains the two gradient styles in more depth, and MLX vs JAX compares MLX with the framework it borrowed them from.

Speed tools: mx.compile and torch.compile

Both frameworks have a compiler you can switch on with one line.

The MLX compile docs say mx.compile merges common work, fuses operations and caches the result, which in many cases improves run time and memory use. The first call is slow, and later calls reuse the compiled graph unless shapes, dtypes or the number of inputs change.

torch.compile traces your model and generates fused kernels. The 2.15 notes list several torch.compile fixes on MPS, such as wrong argmax indices and channels_last convolution backward errors, which tells you compile on the Mac is supported but younger than on CUDA.

Because there is no official benchmark between the two, test your own model. Warm up first, force the work to finish before you stop the timer (mx.eval in MLX, torch.mps.synchronize() in PyTorch), and compare each framework with and without its compiler. Our tinygrad vs PyTorch performance guide walks through fair benchmarking.

Running and fine tuning LLMs on a Mac

For local language models, this is where most readers decide.

MLX has MLX LM, which loads models straight from the Hugging Face Hub, converts and quantizes them, and runs LoRA and full fine tuning, including on quantized models. Its README says thousands of ready models are available in the MLX Community organisation on Hugging Face. A first chat looks like this:

pip install mlx-lm
mlx_lm.chat --model mlx-community/Llama-3.2-3B-Instruct-4bit

Converting a Hugging Face model yourself is one command, mlx_lm.convert --model <repo> -q. Desktop apps such as LM Studio also run MLX models. For how MLX model files compare with GGUF on a Mac, see MLX vs GGUF on Mac.

PyTorch has the far larger ecosystem: Transformers, PEFT, torchtune, TRL and most research code. On a Mac those tools run on the MPS backend, though support varies, so check each library's docs for the MPS device. The Unsloth README now lists macOS support and MLX among its formats, and Unsloth's requirements page lists Mac training through MLX in Unsloth Studio, so even PyTorch first tools are reaching for MLX on the Mac. In practice, many people fine tune with MLX LM on the Mac and use PyTorch on a rented or cloud NVIDIA GPU, such as Google Colab, for bigger jobs. Our guide on how to fine tune an LLM locally covers both routes.

Using MLX and PyTorch together

You do not have to choose one forever. The MLX docs on NumPy and other frameworks explain that PyTorch can import MLX arrays through DLPack with torch.as_tensor, and MLX can import PyTorch tensors with mx.asarray or mx.from_dlpack. When a PyTorch MPS tensor is passed to mx.asarray, MLX imports it without a copy as long as the underlying Metal buffer is not private. mx.array and torch.tensor always copy.

That makes a mixed workflow realistic: keep data loading and a library you need in PyTorch, and hand tensors to MLX for the part that runs best there. For model weights, the common bridge is the safetensors format on Hugging Face, which MLX LM converts from directly.

Install and requirements

MLX on a Mac needs Apple silicon, macOS 14 or newer and a native arm64 Python 3.10 or newer:

pip install mlx
python -c "import mlx.core as mx; print(mx.default_device())"

If pip finds no matching file, the MLX install guide says you are probably using a non native (Intel) Python. python -c "import platform; print(platform.processor())" should print arm.

PyTorch 2.15 needs Python 3.11 or newer, because Python 3.10 wheels are no longer published. For GPU use on a Mac you also need macOS 15 or newer:

pip install torch
python -c "import torch; print(torch.backends.mps.is_available())"

If that prints False on macOS 14, either upgrade macOS or stay on PyTorch 2.14, as the release notes advise.

Pros and cons

MLX

• Pros: made for Apple silicon and its shared memory; NumPy and PyTorch style APIs; MLX LM for running, quantizing and fine tuning LLMs; Swift, C and C++ APIs for apps; MIT licence; CUDA backend on Linux.
• Cons: much smaller ecosystem of libraries and papers; no AMD or Intel GPU support; most third party training tools assume PyTorch.

PyTorch

• Pros: the standard for research and open model tooling; runs on NVIDIA, AMD, Intel, Apple and CPU; eager mode makes debugging simple; torch.compile for speed; huge number of tutorials.
• Cons: on a Mac it uses the separate MPS device instead of treating memory as shared; MPS lacks float64 and now needs macOS 15; several 2.15 highlights, such as faster NVFP4 decoding, are for NVIDIA Blackwell GPUs only.

Which should you choose?

1. You want to run or fine tune open models on your Mac: MLX with MLX LM.
2. You are building a Mac or iOS app with a model inside: MLX, for the Swift and C APIs.
3. Your code must also run on NVIDIA or AMD servers: PyTorch.
4. You are learning deep learning or following papers: PyTorch first, then try MLX on your Mac.
5. You want both: develop in PyTorch, move hot paths or local inference to MLX, and pass tensors through DLPack.

For a wider view of the field, see our best ML frameworks for Python roundup, and our tinygrad vs MLX comparison if you are weighing other small frameworks for Apple silicon.

FAQ

Is MLX faster than PyTorch on a Mac?

There is no official benchmark between them, so the honest answer is that it depends on your model. MLX is designed around Apple silicon's shared memory, and PyTorch 2.15 added faster MPS kernels and a quicker MoE decode path. Time both with warmup excluded and the GPU synced before you decide.

Does PyTorch use the GPU on Apple silicon?

Yes, through the MPS backend: create tensors with device="mps". From PyTorch 2.15 that backend needs macOS 15 or newer, and it does not support float64.

Can I convert a PyTorch model to MLX?

For language models, yes: mlx_lm.convert takes a Hugging Face model and writes MLX weights, with optional quantization. For your own models, rebuild the layers in mlx.nn and load the saved weights. For single tensors, DLPack lets MLX and PyTorch share data, often without a copy.

Does MLX work on Windows or with NVIDIA GPUs?

MLX has a CUDA backend for NVIDIA GPUs on Linux (compute capability 7.5 or newer) and a CPU package for Linux. The install guide does not list Windows.

Should I learn MLX or PyTorch first?

Learn PyTorch first if you want skills that carry across jobs, papers and hardware. Learn MLX first if your only goal is running and tuning models on your own Mac. MLX's layers mirror PyTorch, so moving between them is not hard.

Are MLX and PyTorch free for commercial use?

Yes. MLX uses the MIT License and PyTorch uses a BSD style licence, and both allow commercial use. Model weights you run with them carry their own licences, so check each model card.