AI Blogs - Page 2#
FlyDSL Mono Kernels for High-Interactivity LLM Serving in ATOM
FlyDSL mono kernels cut decode latency in ATOM, with MiniMax-M3 and DeepSeek-V4.1 results.
Device Memory Load Issue Stalls and Scheduling Opportunities in Lock-Stepped Kernels on AMD Instinct™ MI300X
Diagnosing and reducing device-memory load issue stalls in lock-stepped kernels on AMD Instinct MI300X by interleaving compute.
Faster Stable Diffusion on AMD Ryzen™ AI with TeaCache
In this blog, you will learn how to speed up Stable Diffusion 3 and 3.5 on AMD Ryzen AI NPUs with TeaCache, without retraining.
How to Scale Diffusion Inference Across AMD RDNA 4 GPUs
Run xDiT image and video diffusion workloads on multiple RDNA 4 GPUs with practical guidance for latency, memory, and scaling.
Bringing the MONAI Model Zoo to AMD Instinct™ GPUs: Validated Inference on ROCm
Learn how to run validated inference for five MONAI Model Zoo bundles on AMD Instinct™ GPUs using a ROCm overlay.
DeepSeek-V4 Flash RL on AMD Instinct MI308X GPUs with an AITER-Backed RL Stack
Run end-to-end DeepSeek-V4 Flash reinforcement learning with an AITER-backed AMD RL stack on AMD Instinct MI308X GPUs.
Two Waves, One Stage Apart: Warp-Pipelining Triton GEMMs on AMD Instinct
Use warp-pipelining to overlap matrix work and data preparation in compute-bound Triton GEMMs on AMD Instinct GPUs.
ROCm 10.0.0 Support for Ray 2.58.0: Enabling Scalable AI Workflows
ROCm 10.0.0 with Ray 2.58.0 continues PyTorch 2.12 and vLLM support.
Training EAGLE3 Speculative Decoding Draft Models on AMD Instinct MI350X with AMD Quark
Learn how the AMD Quark team trains EAGLE3.1 draft models from scratch on AMD Instinct MI350X GPUs using ROCm, vLLM, TorchSpec, and SPEED-Bench.
Group-GEMM in XLA: Making Mixture-of-Experts Fast and Memory-Efficient
Learn how to make JAX/MaxText MoE training more efficient with two new OpenXLA Group-GEMM backends for ragged-dot: ROCm hipBLASLt and generic Triton.
Accelerating MONAI Digital-Pathology Pipelines on AMD Instinct™ GPUs with hipCIM
In this blog you will measure a MONAI digital-pathology pipeline on AMD Instinct™ GPUs using hipCIM as the WSIReader backend.
Ultralytics YOLO on AMD GPUs: Train, Run, and Deploy with ROCm™
Learn how to train, run, and deploy Ultralytics YOLO26 models on AMD GPUs with PyTorch on ROCm and MIGraphX, starting with Ultralytics 8.4.171.