Skip to main content

MAX changelog

The MAX platform is a unified set of tools and libraries that unlock performance, programmability, and portability for your AI inference pipeline. It includes several products, including MAX Engine, MAX Serving, and the Mojo programming language.

This page describes all the changes in each version of the MAX platform.

To learn more about the platform, read What is MAX.


If you already have MAX, see how to update. If you don't have it yet, see the install guide.

v24.4 (2024-06-07)

🔥 Legendary

  • MAX is now available on macOS! Install now.

  • New quantization APIs for MAX Graph. You can now build high-performance graphs in Mojo that use the latest quantization techniques, enabling even faster performance and more system compatibility for large models.

    Learn more in the guide to quantize your graph weights.

⭐️ New



Miscellaneous new APIs:

  • M_cloneCompileConfig()
  • M_copyAsyncTensorMap()
  • M_tensorMapKeys() and M_deleteTensorMapKeys()
  • M_setTorchLibraries()

🦋 Changed


  • and functions that return a type-erased pointer were renamed to unsafe_ptr().

  • TensorMap now conforms to CollectionElement trait to be copyable and movable.

  • custom_nv() was removed, and its functionality moved into custom() as an function overload, so it can now output a list of tensor symbols.

Mojo updates

For all the Mojo language and library changes in this release, see the Mojo changelog.

v24.3 (2024-05-02)

🔥 Legendary

  • You can now write custom ops for your models with Mojo!

    Learn more about MAX extensibility.

🦋 Changed

  • Added support for named dynamic dimensions. This means you can specify when two or more dimensions in your model's input are dynamic but their sizes at run time must match each other. By specifying each of these dimension sizes with a name (instead of using None to indicate a dynamic size), the MAX Engine compiler can perform additional optimizations. See the notes below for the corresponding API changes that support named dimensions.

  • Simplified all the APIs to load input specs for models, making them more consistent.

MAX Engine performance

  • Compared to v24.2, MAX Engine v24.3 shows an average speedup of 10% on PyTorch models, and an average 20% speedup on dynamically quantized ONNX transformers.


The max.graph APIs are still changing rapidly, but starting to stabilize.

See the updated guide to build a graph with MAX Graph.

  • AnyMoType renamed to Type, MOTensor renamed to TensorType, and MOList renamed to ListType.

  • Removed ElementType in favor of using DType.

  • Removed TypeTuple in favor of using List[Type].

  • Removed the Module type so you can now start building a graph by directly instantiating a Graph.

  • Some new ops in max.ops, including support for custom ops.

    See how to create a custom op in MAX Graph.

MAX Engine Python API

  • Redesigned InferenceSession.load() to replace the confusing options argument with a custom_ops_path argument for use when loading a custom op, and an input_specs argument for use when loading TorchScript models.

    As a result, CommonLoadOptions, TorchLoadOptions, and TensorFlowLoadOptions have all been removed.

  • TorchInputSpec now supports named dynamic dimensions (previously, dynamic dimension sizes could be specified only as None). This lets you tell MAX which dynamic dimensions are required to have the same size, which helps MAX better optimize your model.

MAX Engine Mojo API

MAX Engine C API

❌ Removed

  • Removed TensorFlow support in the MAX SDK, so you can no longer load a TensorFlow SavedModel for inference. However, TensorFlow is still available for enterprise customers.

    We removed TensorFlow because industry-wide TensorFlow usage has declined significantly, especially for the latest AI innovations. Removing TensorFlow also cuts our package size by over 50% and accelerates the development of other customer-requested features. If you have a production use-case for a TensorFlow model, please contact us.

  • Removed the Python CommonLoadOptions, TorchLoadOptions, and TensorFlowLoadOptions classes. See note above about InferenceSession.load() changes.

  • Removed the Mojo LoadOptions type. See the note above about InferenceSession.load() changes.

v24.2.1 (2024-04-11)

  • You can now import more MAX Graph functions from max.graph.ops instead of using max.graph.ops.elementwise. For example:

    from max.graph import ops

    var relu = ops.relu(matmul)

v24.2 (2024-03-28)

  • MAX Engine now supports TorchScript models with dynamic input shapes.

    No matter what the input shapes are, you still need to specify the input specs for all TorchScript models.

  • The Mojo standard library is now open source!

    Read more about it in this blog post.

  • And, of course, lots of Mojo updates, including implicit traits, support for keyword arguments in Python calls, a new List type (previously DynamicVector), some refactoring that might break your code, and much more.

    For details, see the Mojo changelog.

v24.1.1 (2024-03-18)

This is a minor release that improves error reports.

v24.1 (2024-02-29)

The first release of the MAX platform is here! 🚀

This is a preview version of the MAX platform. That means it is not ready for production deployment and designed only for local development and evaluation.

Because this is a preview, some API libraries are still in development and subject to change, and some features that we previously announced are not quite ready yet. But there is a lot that you can do in this release!

This release includes our flagship developer tools, currently for Linux only:

  • MAX Engine: Our state-of-the-art graph compiler and runtime library that executes models from PyTorch and ONNX, with incredible inference speed on a wide range of hardware.

    • API libraries in Python, C, and Mojo to run inference with your existing models. See the API references.

    • The max benchmark tool, which runs MLPerf benchmarks on any compatible model without writing any code.

    • The max visualize tool, which allows you to visualize your model in Netron after partially lowering in MAX Engine.

    • An early look at the MAX Graph API, our low-level library for building high-performance inference graphs in Mojo.

  • MAX Serving: A preview of our serving wrapper for MAX Engine that provides full interoperability with existing AI serving systems (such as Triton) and that seamlessly deploys within existing container infrastructure (such as Kubernetes).

    • A Docker image that runs MAX Engine as a backend for NVIDIA Triton Inference Server. Try it now.
  • Mojo: The world's first programming language built from the ground-up for AI developers, with cutting-edge compiler technology that delivers unparalleled performance and programmability for any hardware.

    • The latest version of Mojo, the standard library, and the mojo command line tool. These are always included in MAX, so you don't need to download any separate packages.

    • The Mojo changes in each release are often quite long, so we're going to continue sharing those in the existing Mojo changelog.

Additionally, we've started a new GitHub repo for MAX, where we currently share a bunch of code examples for our API libraries, including some large model pipelines such as Stable Diffusion in Mojo and Llama2 built with MAX Graph. You can also use this repo to report issues with MAX.

To get a peek at what's coming soon, and learn about some of the bugs we're working on right now, see the MAX roadmap & known issues.