# New Release: Ultralytics v8.4.171

**URL:** <https://community.ultralytics.com/t/new-release-ultralytics-v8-4-171/2248>\
**Category:** Discussion\
**Tags:** ultralytics-official, releases, announcements\
**Created:** [October 1, 2026, 1:13pm UTC](https://community.ultralytics.com/t/new-release-ultralytics-v8-4-171/2248 "2026-10-01T13:13:12Z")\
**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:** [October 1, 2026, 1:13pm UTC](https://community.ultralytics.com/t/new-release-ultralytics-v8-4-171/2248/1 "2026-10-01T13:13:12Z")

</div>

# 🚀 Ultralytics v8.4.171: AMD GPU Support and More Reliable Workflows

**Ultralytics YOLO v8.4.171 expands hardware support with AMD ROCm and MIGraphX** , bringing AMD GPU acceleration to training and ONNX inference. This release also improves dataset caching, fixes RT-DETR training behavior, and streamlines model export.

The focus is broader hardware compatibility and smoother workflows— **no new model architecture is introduced**.

## 🔥 New Features: AMD GPU Support

[PR #24137 by @itikhono](https://github.com/ultralytics/ultralytics/pull/24137) adds AMD support across training and deployment:

- **Train and predict with ROCm:** Run native PyTorch models on supported AMD GPUs.
- **Accelerate ONNX inference with MIGraphX:** Execution-provider selection is automatic on supported systems, with CPU fallback when MIGraphX is unavailable.
- **Start repeat inference sessions faster:** Compiled MIGraphX models are cached to avoid recompilation on subsequent loads. The first run still requires compilation.
- **Simplify deployment:** A ROCm-based `latest-amd` Docker image, dedicated setup guidance, and AMD GPU CI testing support the new workflow.

MIGraphX support targets **Linux x86\_64 with Python 3.11 or newer**. Published Radeon 8060S benchmarks show matching accuracy and faster ONNX/MIGraphX inference than PyTorch ROCm in the tested configurations.

## 🛠 Bug Fixes and Workflow Improvements

- **Dataset caching and task behavior:** [PR #26461 by @glenn-jocher](https://github.com/ultralytics/ultralytics/pull/26461) addresses regressions identified during third-party PR audits, including image-cache reading across cache modes, archive timestamp preservation to avoid unnecessary label rescans, pose keypoint scaling in LiteRT/RKNN, and mask and analytics behavior.
- **More reliable RT-DETR training:** [PR #26465 by @Nikhi00718](https://github.com/ultralytics/ultralytics/pull/26465) corrects contrastive denoising group indices, fixing denoising query matching during training.
- **Lower temporary storage usage during export:** [PR #26466 by @amanharshx](https://github.com/ultralytics/ultralytics/pull/26466) removes an unnecessary temporary weights copy from Core ML NMS export.

## 📚 Documentation and Testing

- **Apple M4 performance guidance:** [PR #26459 by @onuralpszr](https://github.com/ultralytics/ultralytics/pull/26459) adds Core ML and Core AI latency benchmarks to the Core AI documentation.
- **Cleaner test logs:** [PR #26462 by @UltralyticsAssistant](https://github.com/ultralytics/ultralytics/pull/26462) suppresses expected, benign tracer warnings in pytest, matching the package’s warning handling.

## 🙌 Community Contributions

Thanks to all contributors and the YOLO community for helping improve this release! A special welcome to **@itikhono** , whose first contribution brings the new AMD support.

## ▶ Try v8.4.171

Upgrade to this release:

```bash
pip install --upgrade ultralytics==8.4.171

```

Explore the [v8.4.171 release notes](https://github.com/ultralytics/ultralytics/releases/tag/v8.4.171) for release details, or review the [full changelog](https://github.com/ultralytics/ultralytics/compare/v8.4.170...v8.4.171) to see all changes.

**Give it a try and share your feedback below!** If you’re testing AMD acceleration, include your GPU, operating system, Python version, and workload to help the community compare results.