At GDC this year, we shared how machine learning is becoming foundational to real time graphics, and how DirectX is evolving to meet that shift across shader level and model level ML. ML is no longer a niche optimization or a postprocess trick. It’s increasingly embedded throughout the graphics pipeline, influencing how frames are generated, how content is authored, and how game developers realize their artistic vision. DirectX is evolving to support this future— one where ML is a first-class citizen alongside traditional rendering workloads.

Introducing DX Linear Algebra

Last year, DirectX took a major step into the ML era with the introduction of Cooperative Vector in Shader Model 6.9. For the first time, developers could access hardware accelerated vector–matrix operations directly from HLSL, enabling a class of neural rendering techniques that execute inline with traditional shading. These workloads—such as neural texture compression (DTC) and neural radiance caching (NRC)—map naturally to highly parallel, per-pixel inference.

Cooperative Vector has since demonstrated that ML can be effectively integrated directly into the graphics pipeline, particularly for scenarios where developers want fine-grained, shader level control over how ML is applied alongside traditional rendering logic.

As ML usage expanded, however, it became clear that not all workloads fit this execution model. Many common and emerging scenarios—such as denoising, temporal upscaling, and more—require matrix–matrix operations, shared data across threads, and batch-oriented execution that go beyond what vector–matrix primitives alone can efficiently express.

To address this gap, we introduced DirectX Linear Algebra, an expansion of DirectX’s math capabilities designed to support both vector and matrix-based ML workloads under a single programming model. DX Linear Algebra adds first-class matrix–matrix operations while preserving the ability to author ML directly in HLSL, giving developers explicit control over math, data flow, and execution for shader level ML scenarios. These capabilities establish a scalable foundation for shader‑level ML in DirectX.

Expanding to Model Level ML with DirectX Compute Graph Compiler

While shader-level ML is powerful, many modern ML-driven graphics workloads are best expressed and optimized as full computation graphs, not as isolated operators or hand-authored kernels. These graphs capture end-to-end structure—dataflow, dependencies, and deep fusion—that are difficult or impossible to exploit at the shader level, especially when targeting the full PC ecosystem.

That’s why we introduced DirectX Compute Graph Compiler.

DirectX Compute Graph Compiler is a new DirectX ML compiler API designed to execute full model graphs with native class GPU performance. Models flow from modern frameworks, where DirectX can analyze and specialize the complete graph for a given device before lowering it into optimized workloads that integrate natively with D3D12 queues and command lists.

Key benefits include:

• Dropping full models into engines without shader rewrites
• Automatic graph optimization, memory planning, and operator fusion
• Portable performance across IHVs
• Unified tooling with PIX, showing graphics and ML workloads in a single capture

Shader-level ML and model-level ML now live side by side in DirectX: HLSL Linear Algebra for small, inline workloads and DirectX Compute Graph Compiler for larger models.

Support from our hardware vendor partners

AMD: “DirectX Linear Algebra and DirectX Compute Graph Compiler give developers new ways to integrate machine learning directly into their graphics pipelines while retaining the control and performance characteristics they expect from modern GPUs. We’re excited to collaborate with Microsoft on advancing ML-driven graphics on Windows.” – Robert Shearer, CVP Silicon Design Engineering, AMD

Intel: “DirectX Linear Algebra gives developers a powerful new foundation for bringing matrix-based machine learning directly into real-time graphics workflows. We’re excited to support Linear Algebra on day one.” – Lisa Pearce, Corporate Vice President, Software Group, Intel

For more, see here

NVIDIA: “With DirectX Linear Algebra and DirectX Compute Graph Compiler, developers gain flexible paths to integrate both shader level and model level machine learning seamlessly into their graphics pipelines. We’re pleased to support both capabilities and to collaborate with Microsoft on accelerating ML driven rendering and inference workflows on NVIDIA GeForce RTX GPUs.” – Patrick Neill, Distinguished Engineer, NVIDIA

For more, see here

Qualcomm: “DirectX Compute Graph Compiler is a meaningful step toward making full model ML feel native inside real-time engines. We’re excited to collaborate with Microsoft on a compiler-based approach that takes modern model graphs and produces optimized GPU workloads that integrate directly into DirectX.” – Balaji Calidas, Senior Director of Engineering, Qualcomm

What’s Next

ML is no longer an optional enhancement in rendering; it’s becoming core to how graphics are generated. To support this shift, DirectX is evolving into a platform that delivers efficient ML execution at every scale with first-class tooling and visibility. These layers give developers control over how and where ML is integrated in their pipeline, without sacrificing performance, portability, and artistic intent.

DirectX Compute Graph Compiler will be available for private preview this summer, please reach out to your Windows representative if you’re interested in joining.

DX Linear Algebra will enter public preview in April, giving developers an early opportunity to experiment with these capabilities and help shape the future of ML‑assisted graphics on Windows. See the Linear Algebra spec for more detail about the feature.

We’re excited to continue this journey with our partners and the developer community. Check out our GDC session, and stay tuned to the DirectX blog for deeper dives, samples, and updates.