AMD ROCm Blogs

Semantic Fencing of Video Streams Using Embedding Splits from Vision Foundation Models

2026-05-15T00:00:00+00:00

In this blog, we present a novel approach for semantically splitting vision datasets into training, validation, and test sets. Instead of relying on ad hoc metadata rules or random shuffles, we use embeddings to reason directly about similarity in latent space and construct splits that better reflect true generalization.

Further Accelerating Kimi-K2.5 on AMD Instinct™ MI325X: W4A8 & W8A8 Quantization with AMD Quark

2026-05-14T00:00:00+00:00

In our previous blog [7], we demonstrated how to accelerate Kimi-K2.5 [1] inference on AMD Instinct™ GPUs by profiling the model, identifying fused_moe as the dominant bottleneck (consuming 88–90% of GPU time), and replacing the default Triton-based kernel with a FlyDSL [2]-powered mixed-precision (BF16 + W4A16) fused MoE implementation.

Accelerating ComfyUI Workflows on AMD Instinct™ MI355X GPUs with ROCm

2026-05-11T00:00:00+00:00

ComfyUI is an open-source, graphical node-based interface for building generative AI workflows using diffusion models. With over 100,000 stars on GitHub, it has become one of the most widely adopted tools for text-to-image, text-to-video, and image-to-3D generation. You can build workflows by connecting nodes in a drag-and-drop visual interface (no coding required). A large community contributes custom nodes and workflow templates, making ComfyUI a versatile front-end for models ranging from 12B to 27B parameters. For background and setup across AMD platforms, see the earlier ROCm blogs Running ComfyUI on AMD Instinct, Getting Started with ComfyUI on AMD Radeon™ RX 9000 Series GPUs, and Running ComfyUI in Windows with ROCm on WSL.

vLLM-ATOM: Unlocking Native AMD Performance in the vLLM Ecosystem

2026-05-07T00:00:00+00:00

This blog walks you through vLLM-ATOM, the AMD-optimized plugin that supercharges vLLM on Instinct GPUs.

AMD-Powered 3D Gaussian Splatting for Autonomous Driving Scenes

2026-05-07T00:00:00+00:00

3D Gaussian Splatting (3DGS) is an innovative, explicit scene representation and rendering technique. It reconstructs photorealistic 3D environments from a set of images of the scene from a variety of angles. It represents a scene as a vast, learnable collection of 3D Gaussians, which are optimized with backpropagation using a differentiable rasterizer. This pipeline enables real-time novel view synthesis of the scene – generating images of the scene from previously unseen angles. It also permits easy scene editing by moving, copying, recolouring, etc. parts of the reconstructed 3D structure.

Accelerating Mixture-of-Experts Execution with FarSkip-Collective Models

2026-05-05T00:00:00+00:00

Whether you are running training or inference, the largest Mixture-of-Experts (MoE) based LLMs cannot fit on a single GPU; instead you must run collective-communication operations to integrate the work of multiple GPUs to work together on a single model.

TraceLens: Democratizing AI Performance Analysis

2026-04-27T00:00:00+00:00

Profiling modern AI workloads produces huge traces that are hard to interpret. Framework profilers record thousands of operations, kernels, and communication events, and engineers often end up staring at tools like Perfetto UI doing manual calculations. TraceLens speeds this up: it consumes existing framework traces and turns them into structured summaries and comparisons, allowing you to move on to the actual diagnosis and optimization.

Styled Text Image Generation with Eruku on AMD

2026-04-24T00:00:00+00:00

Producing text images where text is both readable and controllable while faithfully matching a target visual style is a challenging problem. It has broad applications ranging from synthetic handwritten text generation to graphic design. In these settings, you need more than plausible images; you need precise control over both text content and visual fidelity. This is where Eruku [1] stands out.

Primus Projection: Estimate Memory and Performance Before You Train

2026-04-24T00:00:00+00:00

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Getting Started with FlyDSL Nightly Wheels on ROCm

2026-04-20T00:00:00+00:00

In the previous post on FlyDSL, we introduced the motivation behind FlyDSL and how it enables Python-native GPU kernel development using the AMD ROCm™ software stack. FlyDSL combines the flexibility of Python with the performance of MLIR and LLVM-based compilation, allowing developers to write GPU kernels in Python while targeting modern AMD hardware.