MLX vs JAX: Which Framework Should You Pick?

In the MLX vs JAX choice, pick MLX if your models run on an Apple silicon Mac: it is Apple's array framework, it uses the memory the Mac's CPU and GPU share, and MLX LM gives you ready commands to run and fine tune open language models. Pick JAX if you train on Google TPUs or NVIDIA GPUs, need to spread one model across many chips, or work in a lab whose code is already JAX. The two look alike on paper because MLX borrowed JAX's style of composable function transforms, but they aim at different hardware, and the JAX team's own install guide says JAX does not support the Mac GPU.

That last point settles the question for many readers. If you own a Mac and want GPU speed, JAX runs only on the CPU there, so MLX wins by default. If you need TPUs or a cluster, MLX is not built for that job. The rest of this guide covers where the frameworks overlap, where they differ and how to move between them. We checked versions and requirements against each project's official docs and release pages on November 11, 2026, and we ran a small side by side test on CPU (details below). We did not run GPU or TPU benchmarks, and neither project publishes an official comparison with the other.

MLX vs JAX at a glance

Versions and Python floors come from the MLX releases page, the JAX releases page and each package's PyPI listing. Hardware rows come from the MLX install guide and the JAX installation guide.

What MLX and JAX have in common

Both frameworks treat a model as a function and get gradients by transforming that function. In JAX you call jax.grad(loss) and get back a new function that returns gradients. In MLX you call mx.grad(loss) and get the same thing. Both have vmap for vectorizing over a batch, vjp and jvp for lower level derivatives, value_and_grad, and checkpoint for trading compute for memory. The MLX README names JAX among its inspirations, along with NumPy, PyTorch and ArrayFire.

Both also follow NumPy closely, so jnp.mean and mx.mean read the same way. If you can write one, you can read the other within an hour.

In our CPU test, the same mean squared error loss produced matching gradient shapes in both, and stacking vmap on grad gave per example gradients of the same shape in both:

import jax, jax.numpy as jnp
import mlx.core as mx

def jloss(w, x, y): return jnp.mean((x @ w - y) ** 2)
def mloss(w, x, y): return mx.mean((x @ w - y) ** 2)

# per example gradients: one gradient per row of x
jax.vmap(jax.grad(jloss), in_axes=(None, 0, 0))(jw, jx[:, None, :], jy[:, None, :])
mx.vmap(mx.grad(mloss), in_axes=(None, 0, 0))(mw, mx_x[:, None, :], my[:, None, :])

That shared style is why people compare the two at all. It is also why moving research code between them is easier than moving it to or from PyTorch.

Where they differ

Hardware

This is the biggest difference. MLX was built for Apple silicon. Its install guide asks for an Apple silicon Mac, macOS 14 or newer and a native arm64 Python. It also runs on Linux: pip install mlx[cuda12] or mlx[cuda13] for NVIDIA cards from architecture SM 7.5 upward, and pip install mlx[cpu] for machines without a GPU.

JAX was built around Google's XLA compiler, and its support table lists TPUs and NVIDIA GPUs on Linux as fully supported, AMD GPUs on Linux as supported through a ROCm plugin, and Intel GPUs as experimental. The same table marks Apple GPU as experimental, but the Mac GPU section of the guide says plainly that JAX is not supported on the Mac GPU and that you should use the standard CPU install. Apple's jax-metal plugin has not had a release on PyPI since version 0.1.1 in October 2024. In practice, JAX on a Mac means CPU only.

On Windows, JAX's CPU wheels are marked experimental, and NVIDIA GPU use goes through WSL2, also experimental. MLX has no Windows build.

Arrays, state and randomness

JAX arrays are immutable. In our test, b[1] = 5 on a JAX array raised a TypeError, and the JAX way is b = b.at[1].set(5), which returns a new array. MLX arrays accept ordinary item assignment, so a[1] = 5 just works.

Random numbers follow the same split. JAX makes you create a key with jax.random.key(0), split it and pass a fresh key to every random call, which keeps results reproducible under compilation and across devices but adds bookkeeping. MLX keeps a global random state you set with mx.random.seed, and you can pass explicit keys when you want them. The JAX gotchas page covers both rules, and the JAX README warns that the project has "sharp edges."

Laziness and compilation

MLX is lazy: operations build a graph, and nothing runs until you call mx.eval or need a value. JAX dispatches operations as you call them, then you wrap hot functions in jax.jit to compile them with XLA.

Both compilers recompile when input shapes change. In our test, jax.jit traced a function three times for inputs of length 10, 10, 20 and 30, once per new shape. The MLX compile docs say the same of mx.compile, and add a shapeless=True option that compiles once for inputs of any shape, with a warning that code depending on shapes will then misbehave. Without mx.compile, MLX builds its graph fresh each call, so changing shapes costs nothing extra. Our guide to the best ML framework for varying batch size covers this tradeoff across more frameworks.

Layers and training loops

MLX ships mlx.nn and mlx.optimizers in the box, with an API that the README says closely follows PyTorch. JAX itself has no layer library. You choose Flax (whose docs recommend the newer NNX API), Equinox, or Keras 3 with JAX as its backend, and Optax for optimizers. That choice gives flexibility but means more decisions on day one.

Scaling past one machine

JAX's strength is large training runs. Its sharding tools, jax.make_mesh, NamedSharding and shard_map, let one program run across many TPU or GPU chips, and MaxText builds on them to scale LLM training across TPU clusters.

MLX has a distributed module too. Its distributed docs list four backends: MPI, a ring backend over TCP sockets, JACCL for low latency RDMA over Thunderbolt (which the docs say is needed for things like tensor parallelism), and NCCL for CUDA machines. That makes it practical to link a few Macs for a large model, but it is not aimed at TPU pods or big GPU clusters.

Running and fine tuning LLMs

For most readers of this site, this decides it.

On a Mac, MLX LM loads models from the Hugging Face Hub, converts and quantizes them, and runs LoRA and full fine tuning:

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

Version 0.32.2 (October 16, 2026) is current and needs Python 3.11 or newer. Desktop apps such as LM Studio also load MLX models. For how MLX model files compare with GGUF, see MLX vs GGUF on Mac, and for Mac fine tuning routes, see how to fine tune an LLM locally.

JAX's LLM tools aim at accelerators. MaxText is Google's JAX reference for training models such as Gemma, Llama, DeepSeek and Qwen on TPUs and GPUs, and Tunix is a JAX library for post training, including supervised fine tuning and reinforcement learning. If you want to try them without hardware of your own, Kaggle offers free TPU time and Google Colab has TPU runtimes.

Neither framework has the ecosystem PyTorch has. Hugging Face said Transformers v5 would sunset its Flax support, as our PyTorch vs JAX guide explains, and most fine tuning tools are PyTorch first.

Install and requirements

MLX on a Mac (Apple silicon, macOS 14 or newer, native Python 3.10 or newer):

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

MLX on Linux: pip install mlx[cuda13] (driver 580 or newer), mlx[cuda12], or mlx[cpu]. The CPU and CUDA packages need glibc 2.35 or newer.

JAX needs Python 3.12 or newer for version 0.12:

pip install -U jax              # CPU, including Macs
pip install -U "jax[cuda13]"    # NVIDIA GPU on Linux
pip install -U "jax[tpu]"       # Google Cloud TPU VM

Both installed from pip in under a minute in our Linux test, and both default to the CPU when no supported accelerator is present.

Pros and cons

MLX

• Uses the Mac GPU and the full unified memory pool, so large models fit without a separate device copy.
• Layers and optimizers built in, with a PyTorch style API.
• MLX LM makes local LLM work quick.
• Also runs on NVIDIA under Linux, but its community and tools center on the Mac.
• Not built for TPUs or large clusters; no Windows build.

JAX

• First class TPU support and mature sharding for very large training runs.
• Pure functions make results reproducible and transforms composable.
• Strong research use, with MaxText and Tunix for LLMs.
• No Mac GPU support, and native Windows is CPU only.
• More choices up front (layer library, optimizer library) and stricter rules on state and randomness.

Which should you choose?

• You have a Mac and want to run or fine tune models: MLX.
• You train on TPUs, or across many GPUs: JAX.
• You are learning functional ML ideas: either works. Write the same model in both; the concepts carry over.
• You prototype on a Mac and train in the cloud: a common split is MLX locally and JAX (or PyTorch) on rented hardware. Expect to rewrite layer code, since mlx.nn and Flax differ, but the math in your loss functions ports almost line by line.
• You mostly need open model tooling: neither. Start with PyTorch, then read MLX vs PyTorch for the Mac side.

If MLX's rivals on the Mac interest you, tinygrad vs MLX covers the other small framework with Apple GPU support.

FAQ

Can JAX use the GPU on a Mac?

Not in a supported way. The JAX installation guide says JAX is not supported on the Mac GPU and tells Mac users to install the CPU version. Apple's jax-metal plugin has had no PyPI release since 0.1.1 in October 2024. On Apple silicon, MLX is the framework that uses the GPU.

Is MLX based on JAX?

No. MLX is a separate framework written by Apple's machine learning research team, with its own C++ core and Metal kernels. Its README lists JAX as one inspiration, which is why its grad, vmap and compile transforms look familiar to JAX users.

Can MLX run on TPUs or NVIDIA GPUs?

MLX does not run on TPUs. It does run on NVIDIA GPUs under Linux through its CUDA packages, for cards from architecture SM 7.5 upward, according to its install guide.

Is it easy to port JAX code to MLX?

The functional parts port well: jnp calls map to mx calls, and jax.grad and jax.vmap have MLX counterparts with similar arguments. Layer code needs rewriting, because Flax or Equinox models do not match mlx.nn. Random keys and .at[].set() updates can be simplified, since MLX allows global seeding and in place assignment.

Which is faster, MLX or JAX?

There is no fair public benchmark, because they rarely run on the same hardware. On a Mac, MLX uses the GPU and JAX does not, so MLX will usually win there. On TPUs, only JAX runs. On NVIDIA GPUs both run, and you should time your own model with warmup and a forced sync (mx.eval or block_until_ready()).

Are MLX and JAX free for commercial use?

Yes. MLX uses the MIT licence and JAX uses Apache 2.0, both permissive open source licences that allow commercial use.