Software tools & optimizations - Page 2#
Discover the latest blogs about ROCm software tools, libraries, and performance optimizations to help you get the most out of your AMD hardware.
Primus Tuning Agent: Closing the Configuration-Search Loop
Use the Primus Tuning Agent to automatically find optimal LLM training configurations on AMD Instinct GPUs.
AgentKernelArena: Benchmarking AI Coding Agents for GPU Kernel Optimization on AMD Instinct GPUs
Explore how AI coding agents compare on real GPU kernel optimization with AgentKernelArena, AMD's open benchmarking arena for Instinct™ GPUs.
Optimizing MI300X Inter-Chiplet Communication via the RCCL Tuner API
Learn how to build a topology-aware RCCL tuner plugin for MI300X CPX/NPS4 mode and validate it with rccl-tests.
Accelerating LLM Inference on AMD GPUs with Low-Latency GEMMs
Learn how FlyDSL low-latency GEMMs speed up LLM decode on AMD GPUs with Split-K, K-slice parallelism, and an LDS-based pipeline.
OpenXLA and JAX - ROCm Support and the State of CI
Learn how OpenXLA and JAX run on AMD ROCm: what landed this year, how every PR is gated on real Instinct hardware, and how to get started.
Efficient GPU Utilization With Workload Pre-Emption in AMD Resource Manager
Learn how GPU workload pre-emption in AMD Resource Manager automatically reclaims idle GPU resources and improves cluster utilization.
DP Attention and TBO for DeepSeek-V4 on MI355X
Learn how ATOM improves DeepSeek-V4 inference on AMD Instinct MI355X GPUs with DP Attention scheduling and Two-Batch Overlap.
Building and Deploying Custom hipBLASLt Libraries on AMD Instinct GPUs
Learn how to manage hipBLASLt environments with custom source builds, RPM/DEB packaging, and version switching on AMD Instinct GPUs.
ATOMesh: Unlocking AMD Hardware for Scalable LLM Serving
Learn how ATOMesh unlocks scalable LLM serving on AMD Instinct GPUs through distributed inference orchestration and ROCm-native execution.
ATOM: Unlocking Extreme AMD Instinct Inference with Software-Hardware Co-Optimization
A technical walkthrough of ATOM on AMD Instinct GPUs, covering architecture, feature scope, model coverage, and practical benchmark dashboard usage.
Dropless MoE Training in JAX with Primus-Turbo
Learn how to train dropless MoE in JAX/MaxText with Primus-Turbo's grouped GEMM and DeepEP all-to-all for faster, more memory-efficient training.
Adapting AIM LLMs For Specific Use Cases Through Fine-Tuning in AMD AI Workbench
Learn how to adapt and fine-tune an AIM LLM in AMD AI Workbench GUI for specialization or specific use cases.