AI Blogs#
Efficient LLM Serving with MTP: DeepSeek V3 and SGLang on AMD Instinct GPUs
This blog will show you how to speed up LLM inference with Multi-Token Prediction in DeepSeek V3 & SGLang on AMD Instinct GPUs
Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows
Ray, combined with ROCm, provides a powerful platform for scaling AI applications, particularly for training and inference workloads.
Technical Dive into AMD's MLPerf Inference v5.1 Submission
In this blog, we share the technical details of how we accomplish the results in our MLPerf Inference v5.1 submission.
Slim Down Your Llama: Pruning & Fine-Tuning for Maximum Performance
This blog describes the technical details of how we prune and fine tune the Llama 3.1 405B model in our MLPerf Inference v5.1 submission.
Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration
performance optimizations for llama.cpp on AMD Instinct GPUs
Day 0 Developer Guide: Running the Latest Open Models from OpenAI on AMD AI Hardware
Day 0 support across our AI hardware ecosystem from our flagship AMD InstinctTM MI355X and MI300X GPUs, AMD Radeon™ AI PRO R700 GPUs and AMD Ryzen™ AI Processors
Unlocking GPU-Accelerated Containers with the AMD Container Toolkit
Simplify GPU acceleration in containers with the AMD Container Toolkit—streamlined setup, runtime hooks, and full ROCm integration.
AMD ROCm: Powering the World's Fastest Supercomputers
Discover how ROCm drives the world’s top supercomputers, from El Capitan to Frontier, and why its shaping the future of scalable, open and sustainable HPC
Reproducing the AMD Instinct™ GPUs MLPerf Inference v5.1 Submission
In this blog, we will provide step by step instruction on how to reproduce AMD's MLPerf Inference v5.1 Submission
Step-3 Deployment Simplified: A Day 0 Developer’s Guide on AMD Instinct™ GPUs
Learn how to deploy Step-3, a 321B-parameter VLM with MFA & AFD, on AMD Instinct™ GPUs to cut decoding costs and boost long-context reasoning
QuickReduce: Up to 3x Faster All-reduce for vLLM and SGLang
Quick Reduce speeds up LLM inference on AMD Instinct™ MI300X GPUs with inline-compressed all-reduce, cutting comms overhead by up to 3×
Introducing AMD EVLM: Efficient Vision-Language Models with Parameter-Space Visual Conditioning
A novel approach that replaces visual tokens with perception-conditioned weights, reducing compute while maintaining strong vision-language performance.
GEMM Tuning within hipBLASLt - Part 1
We introduce a hipBLASLt tuning tool that lets developers optimize GEMM problem sizes and integrate them into the library.
Unleashing AMD Instinct™ MI300X GPUs for LLM Serving: Disaggregating Prefill & Decode with SGLang
Learn how prefill–decode disaggregation improves LLM inference by reducing latency, enhancing throughput, and optimizing resource usage.
AITER-Enabled MLA Layer Inference on AMD Instinct MI300X GPUs
AITER boosts DeepSeek-V3’s MLA on AMD MI300X GPUs with low-rank projections, shared KV paths & matrix absorption for 2× faster inference.
Primus: A Lightweight, Unified Training Framework for Large Models on AMD GPUs
Primus streamlines LLM training on AMD GPUs with unified configs, multi-backend support, preflight validation, and structured logging.
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