Software tools & optimizations - Page 4#
Discover the latest blogs about ROCm software tools, libraries, and performance optimizations to help you get the most out of your AMD hardware.
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.
Performance Profiling on AMD GPUs - Part 4: Fortran OpenMP Offload Edition
Guides developers through profiling and optimizing Fortran OpenMP GPU offload applications using ROCm tools
Deep Dive Into 4-Wave Interleave FP8 GEMM
Learn how to build faster FP8 GEMM kernels on AMD CDNA™4 using 4-wave interleaving to hide memory latency and maximize Matrix Core utilization.
From Build to Benchmark: ONNX Model Serving with Triton Inference Server on AMD GPUs
Step-by-step guide to building, deploying, and benchmarking ONNX models with Triton Inference Server and MIGraphX on AMD GPUs
From Naive to Near-Peak: Building High-Performance GEMM Kernels with Gluon
Learn how a Gluon GEMM tutorial teaches profiling-driven AMD GPU optimization from FP16 baseline to BF8 and MXFP4 kernels.
vLLM-ATOM: Unlocking Native AMD Performance in the vLLM Ecosystem
Use ATOM as an out-of-tree vLLM plugin to keep vLLM compatibility while enabling AMD-optimized attention, model execution, and multi-model support including Kimi-K2.5.
TraceLens: Democratizing AI Performance Analysis
Explore how TraceLens automates profiler trace analysis to pinpoint bottlenecks and optimize AI workloads.