AI Blogs - Page 4#
SPIR-V on ROCm: A Portable IR for AMD GPUs
Learn how SPIR-V brings compile-once, specialize-on-device portability to AMD GPUs — with a reproducible HIP benchmark, trade-off analysis, and quick-start guide.
GEAK V3: Agent-Driven, Repository-Level GPU Kernel Optimization across HIP, Triton, and FlyDSL on AMD GPUs
Explore GEAK v3: agent-driven, repository-level GPU kernel optimization across HIP, Triton, and FlyDSL on AMD Instinct™ GPUs.
Multi-Accelerator Support for AIMs and AMD Solution Blueprints
Deploy and run AIMs and AMD Solution Blueprints across AMD Instinct™ GPUs, AMD EPYC™ CPUs, and AMD Radeon™ GPUs
When a Faster Kernel Doesn't Speed Up Serving: Profiling FP8 KV Cache on AMD Instinct MI308X
Learn how a 34% faster FP8 KV cache kernel delivered 0% E2E speedup, and how profiling attribution exposed the hidden dtype-cast cost on MI308X.
Local Image and Video Generation on AMD Ryzen™ AI Max+ Processor (Windows)
Run ComfyUI natively on Windows on AMD Ryzen AI Max+ with ROCm 7.2.1—SDXL, Flux, and video workflows on the Radeon 8060S, no WSL.
GEAK Agent-Driven Optimization of the DeepSeekV4 MLA Kernel
GEAK Agent accelerates DeepSeekV4 MLA kernel optimization with Triton and delivers SGLang E2E gains on AMD GPUs.
Serving NVFP4 Models on AMD Instinct™ MI355 Accelerators
Learn how to serve NVFP4 models on AMD Instinct™ MI355 using an emulation pipeline in vLLM — no format conversion needed.
QuickReduce INT3 Quantization and Benchmarking on MI355
Learn how QuickReduce uses INT3 quantization to accelerate all-reduce communication and evaluate its performance and accuracy on AMD Instinct MI355 GPUs.
Triton-Based Optimization of Video Sparse Attention on ROCm
Optimize video sparse attention on ROCm with GEAK and linear global context for faster, more stable video generation on AMD GPUs.
Fast Image Generation and Editing with SGLang Diffusion on AMD GPUs
Serve and benchmark diffusion models for image generation and editing on AMD Instinct GPUs using SGLang Diffusion on ROCm.
AMD Instinct™ Network Traffic, Congestion Trends, and Harmonics in Scale-Out Networks for AI Training Clusters
Explore how synchronized GPU collectives create harmonic congestion in AI clusters and the strategies to diagnose and mitigate it.
SGLang-ATOM: Bring ROCm-Native Acceleration to SGLang Serving
Explore how SGLang-ATOM connects SGLang serving applications with ROCm-native ATOM execution to accelerate LLM inference on AMD Instinct GPUs.