# Google TPUs now running PyTorch workloads in Meta data centers

> **Key takeaway:** Meta is using Google's custom AI chips via a new PyTorch-native SGLang inference engine, reducing reliance on JAX and Nvidia.

**Published:** 2026-10-22T10:00:00+00:00 
**Source:** Tom's Hardware 
**Category:** open-hardware 
**Canonical URL:** https://fosswire.org/news/google-tpus-now-running-pytorch-workloads-in-meta-data-cente.html 

## Summary
Google's Tensor Processing Units (TPUs) are now being used in production at Meta for AI workloads. This was made possible through a collaboration between Meta, Google, and RadixArk to create a PyTorch-native version of the SGLang inference engine. Additionally, an open-source version of vLLM for TPUs has been announced, allowing PyTorch users to run workloads on Google hardware without needing to migrate to the JAX framework.

## Technical shift from JAX to PyTorch
- New **SGLang-TorchTPU** engine allows Meta to run PyTorch workloads natively on Google hardware.
- An open-source version of **vLLM** for TPUs has been released.
- Previously, TPU support for these engines required Google's **JAX** framework.
- Performance tests on seventh-generation **Ironwood** TPUs showed the vLLM setup reaching 700 tokens per second for the Kimi K3 model using speculative decoding.
- Without speculative decoding, kernels for certain models delivered 1.4 to 2 times the throughput of an Nvidia GB200 baseline at small batch sizes.

## Why it matters
This move, alongside Amazon's open-sourcing of TorchNeuron, aims to reduce the software lock-in provided by Nvidia's CUDA ecosystem by making alternative AI accelerators more accessible to PyTorch developers.