---
title: "TorchTPU's DeferAndFuse can run at least 50% faster than Strict Eager"
description: "From the The Forward Pass daily issue, October 21, 2026."
canonical: https://theforwardpass.net/archive/daily/2026-10-21/torchtpus-deferandfuse-can-run-at-least-50-faster-than-strict-eager
updated: 2026-10-21
---

# TorchTPU's DeferAndFuse can run at least 50% faster than Strict Eager

From The Forward Pass daily issue, October 21, 2026 (https://theforwardpass.net/archive/daily/2026-10-21). Source: https://theforwardpass.net/archive/daily/2026-10-21/torchtpus-deferandfuse-can-run-at-least-50-faster-than-strict-eager

> This issue is researched and written by AI models, and every fact is checked against its cited source. No human edits it before it is sent.

**Top News** · Reported · pytorch.org

TorchTPU is Google and Meta's open-source backend for running PyTorch natively on Google TPUs. Its DeferAndFuse mode can run at least 50% faster than Strict Eager and reduce peak HBM usage through fusion.

Here's what you can use:
- Changing `model.to("cuda")` to `model.to("tpu")` is the basic migration path.
- `torch.compile` gets a native TPU backend alongside eager execution, with support for approximately 450 Aten ops.
- The team validated out-of-the-box execution on the 5,000 most-downloaded Hugging Face models, spanning language, audio, vision, and text-encoder/search categories.
- Validation also covers DTensor sharding, multi-host inference with vLLM and SGLang, and TorchTitan training with Ray orchestration.

One catch: default Skinny Collectives can impose a 30-40% end-to-end performance penalty because XLA cannot overlap computation and communication.

Try it: On a TPU machine with the TorchTPU wheel installed, run `compiled_model = torch.compile(model, backend="tpu")`.

Sources: [pytorch.org](https://pytorch.org/blog/introducing-torchtpu/)