AI Blogs - Page 4#
Accelerating LLM Inference on AMD GPUs with Low-Latency GEMMs
Learn how FlyDSL low-latency GEMMs speed up LLM decode on AMD GPUs with Split-K, K-slice parallelism, and an LDS-based pipeline.
OpenXLA and JAX - ROCm Support and the State of CI
Learn how OpenXLA and JAX run on AMD ROCm: what landed this year, how every PR is gated on real Instinct hardware, and how to get started.
Efficient GPU Utilization With Workload Pre-Emption in AMD Resource Manager
Learn how GPU workload pre-emption in AMD Resource Manager automatically reclaims idle GPU resources and improves cluster utilization.
MXFP6 and MXFP4 Mixed Precision for Accelerating Dense LLMs on AMD Instinct MI355X
W_MXFP4_A_MXFP6 quantization on AMD Instinct MI355X improves LLM throughput and latency while recovering accuracy versus MXFP4.
DP Attention and TBO for DeepSeek-V4 on MI355X
Learn how ATOM improves DeepSeek-V4 inference on AMD Instinct MI355X GPUs with DP Attention scheduling and Two-Batch Overlap.
Faster Kimi-K2.5-W4A8 Decoding with EAGLE3 on AMD Instinct™ MI325X
Add EAGLE3 speculative decoding and three MoE/FMHA kernel-tuning patches to Kimi-K2.5-W4A8 inference on AMD Instinct™ MI325X with SGLang, AITER, and FlyDSL.
A Practical Guide to Running LLMs on AMD Radeon™ GPUs
This guide describes how to run LLMs on AMD Radeon™ GPUs using a range of partner frameworks, tools, and runtimes, with step-by-step setup instructions and performance optimization tips.
Building and Deploying Custom hipBLASLt Libraries on AMD Instinct GPUs
Learn how to manage hipBLASLt environments with custom source builds, RPM/DEB packaging, and version switching on AMD Instinct GPUs.
Comparative Analysis of Scale-Out RoCE Network Traffic Patterns and Loads in Training Large Language Models
Compares RoCE network traffic patterns and loads across GPT-4, Llama 3, DeepSeek-V2, and Grok 4.0 LLM training to guide AI infrastructure design.
Utilizing AMD Schola and UnrealRoboticsLab with AMD ROCm™ Software to Train a Robotic Arm
Learn how to combine MuJoCo physics, Unreal Engine, and Schola to train a 6-DOF robot arm with reinforcement learning on AMD hardware.
ATOMesh: Unlocking AMD Hardware for Scalable LLM Serving
Learn how ATOMesh unlocks scalable LLM serving on AMD Instinct GPUs through distributed inference orchestration and ROCm-native execution.
Technical Dive into AMD's MLPerf Training v6.0 Submission
In this blog, we share the technical details of how we accomplish the results in our MLPerf Training v6.0 submission.