Posts tagged GEMM Tuning
An Educational GEMM Ladder for Helios GPUs
- 14 September 2026
AMD Helios will be an important platform for AI. Helios offers 432 GB of HBM4, 23 TB/s of HBM bandwidth per GPU, and 40 PFLOPs of FP4 compute AMD Instinct™ MI455X GPU. These capabilities will be especially valuable for large frontier models and long-context agentic workloads.
hipBLASLt Online GEMM Tuning
- 19 March 2026
This blog post introduces the integration of hipBLASLt Online GEMM Tuning into LLM frameworks, illustrated through an example implementation of RTP-LLM. Developed by the AMD Quark Team, hipBLASLt Online Tuning provides a user-friendly approach to improving GEMM performance by enabling runtime tuning without requiring additional offline tuning steps.
Day 0 Developer Guide: hipBLASLt Offline GEMM Tuning Script
- 05 November 2025
This blog post focuses on optimizing the performance of a real model using the QuickTune script, illustrated with an example of offline GEMM tuning for the Qwen model on an AMD MI308 GPU. Developed by the AMD Quark Team, the QuickTune script delivers significant GEMM performance improvements with minimal time overhead. QuickTune is an advanced tool for hipBLASLt offline GEMM tuning. It allows users to complete offline tuning with one click, instead of using hipblaslt-bench to tune the model manually.