Recent Posts#
AMD-HybridLM: Towards Extremely Efficient Hybrid Language Models
Explore AMD-HybridLM’s architecture and see how hybridization redefines LLM efficiency and performance without requiring retraining from scratch
ROCm 7.0: An AI-Ready Powerhouse for Performance, Efficiency, and Productivity
Discover how ROCm 7.0 integrates AI across every layer, combining hardware enablement, frameworks, model support, and a suite of optimized tools
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.
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
Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration
performance optimizations for llama.cpp on AMD Instinct GPUs
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.
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
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.
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×