Featured Posts
ROCm 10.1: Breaking the Data-Movement Bottleneck
ROCm 10.1 targets the data-movement bottleneck with AMD Infinity Storage, NUMA-aware memory, and a modernized stack.
Technical Dive into AMD MLPerf Inference v6.1 Submission
Learn about the ROCm optimizations powering dlrm-v3, llama2-70b, and gpt-oss-120b performance.
A Deep Dive into LDS Optimizations on AMD Instinct MI450 GPUs
Learn how to optimize LDS traffic in Gluon kernels on AMD Instinct MI450 GPUs using transposed loads and partition-conflict-free layouts.
ROCm 10.0: A Decade of Open Compute, Built for the Age of Agentic AI
Explore what's new in ROCm 10.0, headlined by ROCm.AI, the ROCm CLI, AMD Skills, and Hyperloom, alongside the ROCm Core SDK and platform-wide improvements
Accelerating GLM-5.2 on AMD Instinct MI355X: EvalScope Benchmarks with Prefix Caching
Learn how to optimize GLM-5.2 FP8 long-context inference on AMD Instinct MI355X GPUs using prefix caching and end-to-end inference stack tuning.
Parallelism for Long-Context Serving: GLM-5.3-Flash on MI300X
Measured throughput of GLM-5.3-Flash on MI300X: how KV-cache capacity shapes TP8 and DP8+EP8 serving, with BF16 and FP8 KV cache.
Introducing the ROCm Handbooks
Discover the ROCm handbooks: book-style HTML and PDF guides that take you from your first HIP kernel to optimizing code for AMD GPUs.
MONAILabel 0.8.5 on AMD Instinct™ GPUs: Interactive Medical-Image Annotation
Learn how to run MONAILabel 0.8.5 for interactive medical-image annotation on AMD Instinct™ GPUs.
Online Quantization: Smooth Path from One Checkpoint to Any Desired Serving Scenario
Learn how AMD Quark's online quantization quantizes LLM weights at load time across vLLM, ATOM, and SGLang, with per-layer mixed FP8/MXFP4 recipes.
Quark Mixed-Precision Quantization for Wan2.2 Video Generation on AMD Instinct GPUs
Apply AMD Quark mixed-precision MXFP4 quantization to Wan2.2-T2V-A14B video generation with calibration-driven block selection.
How to Scale Diffusion Inference Across AMD RDNA 4 GPUs
Run xDiT image and video diffusion workloads on multiple RDNA 4 GPUs with practical guidance for latency, memory, and scaling.
Bringing the MONAI Model Zoo to AMD Instinct™ GPUs: Validated Inference on ROCm
Learn how to run validated inference for five MONAI Model Zoo bundles on AMD Instinct™ GPUs using a ROCm overlay.
PrimusClaw: An Open-Source Managed Agent Runtime for AMD Instinct GPU Clusters
PrimusClaw is an open-source managed agent runtime that hosts durable, sandboxed agent sessions on your own AMD Instinct GPU clusters.
ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration
Learn how ROCm InferaSim predicts LLM serving performance on AMD Instinct GPUs from a few short runs, so you can deploy the best configuration.
From Operator to Kernel: Profile DeepSeek-V4-Flash in Primus with madengine
Profile an eight-layer training proxy derived from the DeepSeek-V4-Flash Primus configuration on eight MI350X GPUs with madengine and TraceLens, then turn operator-to-kernel insights into faster runs.
Performance Profiling on AMD GPUs - Part 7: AI/PyTorch® Edition
Profile and optimize a PyTorch Evoformer (TinyOpenFold) training workload on AMD Instinct GPUs using rocprofv3, rocprof-compute, and rocprof-sys.
DeepSeek-V4 Flash RL on AMD Instinct MI308X GPUs with an AITER-Backed RL Stack
Run end-to-end DeepSeek-V4 Flash reinforcement learning with an AITER-backed AMD RL stack on AMD Instinct MI308X GPUs.
Ultralytics YOLO on AMD GPUs: Train, Run, and Deploy with ROCm™
Learn how to train, run, and deploy Ultralytics YOLO26 models on AMD GPUs with PyTorch on ROCm and MIGraphX, starting with Ultralytics 8.4.171.
Enabling Physical AI Agents with Lemonade
Learn to deploy local agents for interactive robot arm manipulation using the Lemonade framework.
AUP Learning Cloud: Streamlining AI Education on AMD
An all-in-one ROCm JupyterHub platform that deploys GPU-ready AI teaching environments on AMD hardware with one installer and open-source teaching labs.
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