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Democratizing AI Compute Series
Go behind the scenes of the AI industry with Chris Lattner
Modular is rebuilding AI in the face of a new economy
Here in November 2022, we see a continuing onslaught of bad news: significant layoffs of incredible people as companies tighten their belts; companies that raised too much money, too fast, without core fundamentals are dying; and a changing climate where over-tightening rather than under-tightening is seemingly the new normal.
The Case for a Next-Generation AI Developer Platform
AI promised to profoundly change the world, so why hasn’t it?From healthcare to manufacturing, finance, climate, communication, and travel, to how we live and work. AI can help solve any problem that can be represented by data, assuming the right algorithms and enough computational resources.
The future of AI depends on Modularity
Platforms like TensorFlow, PyTorch, and CUDA do not focus on modularity - there, we said it! They are sprawling technologies with thousands of evolving interdependent pieces that have grown organically into complicated structures over time. AI software developers must deal with this sprawl while deploying workloads to server, mobile devices, microcontrollers, and web browsers using multiple hardware platforms and accelerators.
Democratizing Compute
Go behind the scenes of the AI industry in this blog series by Chris Lattner. Trace the evolution of AI compute, dissect its current challenges, and discover how Modular is raising the bar with the world’s most open inference stack.
Matrix Multiplication on Blackwell
Learn how to write a high-performance GPU kernel on Blackwell that offers performance competitive to that of NVIDIA's cuBLAS implementation while leveraging Mojo's special features to make the kernel as simple as possible.
Structured Mojo Kernels
Learn how Mojo simplifies GPU programming with modular kernel architecture, compile-time abstractions, and zero-cost performance across modern GPU hardware.
Software Pipelining for GPU Kernels
Explore software pipelining for GPU kernels from first principles. We formalize dependencies as a graph, solve for the optimal schedule with a constraint solver, and show how it all integrates into MAX via pure Mojo.
Why LLM Inference Needs a New Kind of Router
This series walks through why traditional HTTP routing breaks down under LLM workloads and how Modular Cloud solves it with a three-layer architecture built for cache-aware routing.
TileTensor
This series walks through how Modular built TileTensor, a Mojo tensor type that lets kernel authors express complex memory layouts precisely, safely, and efficiently.
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