Recent Posts - Page 6#
Using ODC to Accelerate AMD SFT Training
Learn how we ported ODC on-demand P2P communication to AMD Instinct MI300X to cut FSDP bubbles and speed up variable-length SFT training.
Quark Support for HuggingFace Diffusers and SVDQuant
Learn how to quantize, save, and reload diffusion models in Quark using its new SVDQuant and HuggingFace Diffusers support.
AUP Learning Cloud: Streamlining AI Education on AMD
An all-in-one ROCm JupyterHub platform that deploys GPU-ready AI teaching environments on AMD hardware with one installer and open-source teaching labs.
VSA: Accelerating Video Diffusion Inference with Sparse Attention on AMD GPUs
Accelerate video diffusion inference with VSA sparse attention: up to 3.31x attention kernel-time speedup on AMD Instinct MI308X GPUs
Reverse-Engineering hipBLASLt TensileLite Kernels: From Solution Name to a Tuning Config
Pin the pool's best kernel into a TensileLite tuning config by decoding its solution name, so an expanded re-tune can only match or beat it.
Introducing AMD CDNA™ 5 and the AMD Helios™ Rackscale Solution
Introducing AMD CDNA 5, the AMD Instinct MI455X GPU, and the AMD Helios rackscale solution: an open, integrated platform for rack-scale AI.
Closing the GPU Cluster Validation Gap: A Kubernetes-Native Approach with CVF
Learn how to validate AMD GPU clusters end-to-end with CVF: hardware acceptance, mesh bandwidth, RDMA, and RCCL testing in one pipeline.
AMD GPU Operator v1.5.0: DRA Support, Automated GPU Node Recovery, and Expanded Kubernetes Infrastructure Control
Discover how AMD GPU Operator v1.5.0 improves GPU scheduling, automates node recovery, and expands Kubernetes control.
Attention Decode on AMD MI450 GPUs: A Gluon Kernel Optimization Guide
Learn how to design a high-performance attention decode kernel on AMD MI450 GPUs using Gluon.
Enabling Language-specific Reasoning in Multilingual Models with Reinforcement Learning
Learn how to train multilingual reasoning models with reinforcement learning and extend context windows on AMD Instinct GPUs.
Introducing Instella-MoE: A State-of-the-Art Fully Open Mixture-of-Experts Language Model
Explore Instella-MoE-16B-A3B, AMD’s fully open 16B MoE LLM with 2.8B active params per token, trained on AMD Instinct™ MI300 & MI325 GPUs.
Serve Kimi-K2.5-MXFP4 on MI355X with ATOM
Serve Kimi-K2.5-MXFP4 on MI355X with ATOM and gfx950 block-scaled FP4 kernels for optimized LLM inference.