AI Development on Windows: From PyTorch and Llama.cpp to Windows ML
Microsoft has introduced experimental support for llama.cpp and GGUF models within the Windows ML framework to enhance local AI inference capabilities. This update allows developers to run open-source models locally on Windows PCs using new task-specific APIs and an OpenAI-compatible endpoint. Additionally, Microsoft has contributed performance improvements to llama.cpp, including CUDA optimizations and support for multi-GPU execution.
- ▪Windows ML now includes experimental llama.cpp support, enabling users to run GGUF models locally through new task-specific APIs.
- ▪Microsoft has collaborated with NVIDIA to deliver performance improvements to llama.cpp, such as CUDA kernel optimization and speculative decoding features.
- ▪The Windows ML Text Generation API accepts both GGUF and ONNX formats and automatically selects the appropriate execution engine for the model.
- ▪Developers can prototype against local on-device models using an OpenAI-compatible endpoint exposed by the new Windows ML APIs.
- ▪Windows ML is designed to support local AI workflows across various hardware components, including GPUs, NPUs, and CPUs from multiple vendors.
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| Original publisher | Microsoft Foundry on Windows Blog |
| Canonical URL | https://devblogs.microsoft.com/foundry-on-windows/build-on-winml-oct-7-26/ |
| Publication time | Wed, 07 Oct 2026 19:40:34 +0000 |
| Retrieval time | 2026-10-07T21:58:14.285Z |
| Last seen | 2026-10-07T21:58:14.285Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | vzO_0dCi4VDy · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Today we are highlighting improvements across a few key open-source projects many of you already use, as well as updates to Windows ML. To empower developers, we must support the broad range of tools for experimentation and exploration across inference and training, in addition to our production grade native inference stack. Available today, Windows adds experimental llama.cpp support to Windows ML so you can run GGUF models locally through new task-specific APIs. Windows ML is the unified, high-performance local AI inferencing framework for Windows. Our experimental Windows-native Runtime API is now in preview for developers who want more control over how models run and compose.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Microsoft Foundry on Windows Blog.
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