Blog

In this blog post, we’ll continue our journey to build a state-of-the-art (SOTA) matmul kernel on NVIDIA Blackwell by exploring the cluster launch control (CLC) optimization. At the end of the post we’ll improve our performance by another 15% and achieve 1772 TFLOPs, exceeding that of the current SOTA.

In this post, we continue on this journey and discuss how to leverage the 2SM technique along with pipelining to increase our performance about 5x and get within 85% of state-of-the-art (SOTA).

In this post we are going to continue our journey and improve our performance by more than 50x our initial kernel benchmark. Along the way we are going to explain more GPU programming concepts and leverage novel Blackwell features.

This series of blog posts will showcase how one can: 1. Write a high-performance GPU kernel on Blackwell that offers performance competitive to that of NVIDIA's cuBLAS implementation. 2. Shows how one can leverage Mojo's special features to make the kernel as simple as possible.

Given time, budget, and expertise from a team of veterans who’ve built this stack before, Modular set out to solve one of the defining challenges of our era: how to Democratize AI Compute. But what does that really mean—and how does it all add up?

In this post, we’ll break down how Python eDSLs work, their strengths and weaknesses, and take a close look at Triton.