AI - Software Tools & Optimizations#
From Theory to Kernel: Implement FlashAttention-v2 with CK-Tile
Learn how to implement FlashAttention-v2 with CK-Tile: minimize memory overhead, maximize compute efficiency, and scale on AMD GPUs
Boosting Llama 4 Inference Performance with AMD Instinct MI300X GPUs
Learn how to boost your Llama 4 inference performance on AMD MI300X GPUs using AITER-optimized kernels and advanced vLLM techniques
Beyond Text: Accelerating Multimodal AI Inference with Speculative Decoding on AMD Instinct™ MI300X GPUs
This blog shows you how to speedup your multimodal models with AMD’s open-source PyTorch tools for speculative decoding on MI300X GPUs
Hands-On with CK-Tile: Develop and Run Optimized GEMM on AMD GPUs
Build high-performance GEMM kernels using CK-Tile on AMD Instinct GPUs with vendor-optimized pipelines and policies for AI and HPC workloads
Unlock Peak Performance on AMD GPUs with Triton Kernel Optimizations
Learn how Triton compiles and optimizes AI kernels on AMD GPUs, with deep dives into IR flows, hardware-specific passes, and performance tuning tips
Speculative Decoding - Deep Dive
This blog shows the performance improvement achieved by applying speculative decoding with Llama models on AMD MI300X GPUs, tested across models, input sizes, and datasets.
Supercharge DeepSeek-R1 Inference on AMD Instinct MI300X
Learn how to optimize DeepSeek-R1 on AMD MI300X with SGLang, AITER kernels and hyperparameter tuning for up to 5× throughput and 60% lower latency over Nvidia H200
AITER: AI Tensor Engine For ROCm
We introduce AMD's AI Tensor Engine for ROCm (AITER), our centralized high performance AI operators repository, designed to significantly accelerate AI workloads on AMD GPUs
AI Inference Orchestration with Kubernetes on Instinct MI300X, Part 3
This blog is part 3 of a series aimed at providing a comprehensive, step-by-step guide for deploying and scaling AI inference workloads with Kubernetes and the AMD GPU Operator on the AMD Instinct platform
Optimized ROCm Docker for Distributed AI Training
AMD updated Docker images incorporate torchtune finetuning, FP8 support, single node performance boost, bug fixes & updated benchmarking for stable, efficient distributed training
Measuring Max-Achievable FLOPs – Part 2
AMD measures Max-Achievable FLOPS through controlled benchmarking: real-world data patterns, thermally stable devices, and cold cache testing—revealing how actual performance differs from theoretical peaks.
How to Build a vLLM Container for Inference and Benchmarking
This post, the second in a series, provides a walkthrough for building a vLLM container that can be used for both inference and benchmarking.