# New Release: Ultralytics v8.4.137

**URL:** https://community.ultralytics.com/t/new-release-ultralytics-v8-4-137/2202
**Category:** Discussion
**Tags:** announcements, ultralytics-official, releases
**Created:** [September 1, 2026, 1:10pm UTC](https://community.ultralytics.com/t/new-release-ultralytics-v8-4-137/2202 "2026-09-01T13:10:12Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![glenn-jocher](https://sea1.discourse-cdn.com/flex001/user_avatar/community.ultralytics.com/glenn-jocher/32/83_2.png) [@glenn-jocher](https://community.ultralytics.com/u/glenn-jocher)
#### Post date: [September 1, 2026, 1:10pm UTC](https://community.ultralytics.com/t/new-release-ultralytics-v8-4-137/2202/1 "2026-09-01T13:10:12Z")

</div>

# 🚀 Ultralytics v8.4.137: Faster CUDA Training by Default

Ultralytics v8.4.137 automatically enables the faster **channels-last memory layout** for CUDA training with PyTorch 1.11 and newer. This update can improve GPU training performance—especially on modern Tensor Core hardware—while retaining clear opt-out and compatibility options.

## 🌟 New Features

### Automatic channels-last CUDA training

The existing `channels_last=None` setting now selects the optimal default automatically:

- `channels_last=None` enables channels-last for supported CUDA training.
- `channels_last=False` explicitly retains the traditional NCHW format.
- `channels_last=True` explicitly requests channels-last, preserving the previous behavior.

This gives Ultralytics YOLO26 users faster training defaults without requiring manual configuration.

## ⚡ Improvements

- **Potentially faster GPU training:** NHWC memory layout can improve convolution performance on compatible NVIDIA GPUs.
- **Safer compatibility:** Automatic activation starts with PyTorch 1.11, avoiding known failures found with older versions during YOLO26 training.
- **Improved resume handling:** `channels_last` can now be updated when resuming a training run.
- **Updated documentation:** Training guides and argument references now explain the automatic CUDA behavior.
- **Minimal implementation impact:** Selection occurs during trainer setup without new arguments, persistent state, helper utilities, or GPU-specific allowlists.

## 🛡 Compatibility and Control

CPU, MPS, and CUDA environments using PyTorch 1.10 or older continue with NCHW by default.

To explicitly keep the traditional layout, use:

```bash
yolo train model=yolo26n.pt data=coco8.yaml channels_last=False

```

The change was broadly validated across CUDA 11.1–13.2, PyTorch 1.8–2.12, Python 3.8–3.13, and a wide range of modern NVIDIA GPUs.

## 🐛 Bug Fixes and Stability

This release does not change model architectures or outputs. Its compatibility-aware defaults reduce the risk of channels-last failures while preserving predictable behavior across unsupported environments.

## 🙌 Contributor Spotlight

Thank you to @glenn-jocher for [PR #26007: Auto-enable channels-last CUDA training](https://github.com/ultralytics/ultralytics/pull/26007).

## 📦 Try v8.4.137

Upgrade with:

```bash
pip install -U ultralytics

```

Explore the [v8.4.137 release details](https://github.com/ultralytics/ultralytics/releases/tag/v8.4.137) or review the [full changes between v8.4.136 and v8.4.137](https://github.com/ultralytics/ultralytics/compare/v8.4.136...v8.4.137).

Give the new CUDA training default a try, compare performance on your hardware, and share your feedback with the Ultralytics community! 🚀