Also the code for part 1-4 are here github.com/modular/modula… will add to the blog posts too.

We know that one of the biggest barriers to programming GPUs is access to hardware: "Code you’ve written for NVIDIA or AMD GPUs should now mostly just work on an Apple🍎 Silicon GPU, assuming no device-specific features were being used." Preview here:👇 forum.modular.com/t/apple-silico…

We're going to devour llama.cpp, mlx, etc. one step at a time!

We beat Nvidia’s cuBLAS kernels on B200s in 170 LOC. Using zero CUDA. Just pure Mojo. Here’s exactly how we went from 1% to 106% of Nvidia benchmark perf from scratch (with code) 👇🧵

We raised $250M to accelerate building AI's unified compute layer! 🔥 We’re now powering trillions of tokens, making AI workloads 4x faster 🚀 and 2.5x cheaper ⬇️ for our customers, and welcomed 10K’s of new developers 👩🏼‍💻. We're excited for the future!

AI workloads need to run across a growing number of hardware architectures, in the datacenter & at the edge. This ‘industrialization of inference’ demands making every FLOP count through increased resilience & lower costs. USIT believes Modular is best positioned to become

Let's gooooooo Modular 🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀

Mathematics. The beauty of September 27, 2025. By Jerome White, Jerome White, Used with permission.

Missing grothendieck.ai now there're Math, Inc. and Axiom ☹️

AI as a multiplier has created a lot of fake talents. Take it away, they collapse. For real talents, they might become less productive!

Hoping to see a light in one of the darkest corner of math i.e. ABC conjecture/dispute at some point in my lifetime!

Aryan V S Thien Tran Makes sense. Mojo gives you the full power of the hardware, it doesn't "abstract" it like some other systems, so it is perfect for doing this sort of work. It provides helper libraries that you can optionally use to make some things (incl tiling etc) more declarative, and

I encourage you to read this article, in which we describe the current situation and the directions in which, in our view, mathematics is heading. Many thanks to Ken Ono for including me in this extraordinary project. I look forward to a wide-ranging discussion and will be

pytorch consulting the math library heuristics which say transposing the matrix will yield a 30% speedup over the non-transposed case, deciding to transpose the matrix, and immediately running out of memory

What we owe the minds we create timdavis.com/blog/what-we-o…

In just 14 days, Modular achieved state-of-the-art performance with AMD MI355X GPUs on @TensorWaveCloud 🚀 Check out the article in the link below 👇

We have a fast growing open source Mojo🔥+MAX🧑‍🚀 community and Modular is hiring for a number of roles! Please check out our careers page: modular.com/company/career…, visit us at PyTorch Con🔥, or LLVM Developer Conference🐉 next week. It's such an exciting time in the industry! 🚀

Yeah, small players punch up like this!