# AMD and Intel agree on oneDNN as PyTorch's shared x86 CPU acceleration library

> OnPrem World · https://onpremworld.com/en/news/2026-10-amd-intel-onednn-pytorch-x86 · CC BY 4.0

Published: 2026-10-20 · Other

AMD and Intel are combining their x86 CPU optimization work for PyTorch in oneDNN, an open-source library governed by the UXL Foundation, and have aligned on it as the default open-source x86 acceleration library in PyTorch. AMD is integrating its ZenDNN optimizations into oneDNN and has joined the UXL Foundation as a steering member. The two companies are also working on AI Compute Extensions, a next-generation x86 instruction set for matrix multiplication, with PyTorch support planned through future oneDNN updates. No release dates, specific processors or performance figures were given.

## Why it matters locally

Anyone running PyTorch inference on AMD or Intel CPUs should end up with one optimized code path instead of two vendor-specific ones.

## For the market

Two long-time rivals sharing one software layer suggests they see more to gain from keeping x86 attractive for AI against Arm-based chips and GPUs than from competing on libraries. It could also make CPU-only servers an easier choice for light inference, though GPUs will likely stay the default for larger models.

## In plain words

AMD and Intel, which make most computer processors, have agreed to put their AI speed-up tricks for a popular AI toolkit into one shared package. It is like two car makers agreeing on one type of charging plug: developers no longer have to build for each brand separately.

## For practitioners

If you serve PyTorch models on EPYC or Xeon today, track which PyTorch and oneDNN releases pick up the ZenDNN work and re-benchmark against your current setup, since a shared library does not guarantee equal speed on both vendors. AI Compute Extensions need new hardware and future oneDNN updates, so nothing changes for existing servers yet.

## Source

[AMD Newsroom: AMD and Intel collaborate to simplify AI development on x86 CPUs using PyTorch](https://newsroom.amd.com/news/amd-intel-collaborate-simplify-ai-development-on-x86-cpus-pytorch/)

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