Training is gradient descent, and gradient descent needs gradients, so every ML framework has an automatic differentiation engine at its core. What is less obvious is that the representation a fram...

If you train or serve models, you depend on MLIR whether or not you have ever written a line of it. XLA lowers through it, Triton is built on it, Mojo is MLIR-native, and Torch-MLIR, IREE, and ONNX...

Python is a productive orchestration language for inference serving, but it is the wrong tool for the critical path of token generation. Large-model inference is bound by memory bandwidth and by pe...

VLIW (Very Long Instruction Word) spent most of the last two decades as a specialist’s tool. After the commercial struggles of general-purpose VLIW designs, with Intel’s Itanium being the canonical...

A step-by-step field guide to building vLLM from source on Ubuntu 26.04, covering Python 3.14 compatibility, CUDA driver issues, and toolchain pitfalls.

How vLLM's op‑level IR reconciles the tension between a compiler target and hand‑tuned kernel dispatch, enabling graph‑level fusion while supporting multiple back‑ends.

If you have a massive compute architecture—whether it’s a modern wide-SIMD vector engine, a Tensor Core array, or a custom deep learning accelerator like a Systolic Array—you face one fundamental p...

In a previous post, we explored the monumental software stack required to run a simple “Hello, World!” program on a modern operating system. But what happens when we apply these concepts to a heter...

In our previous post, we took a deep dive into the hidden complexities of the simplest C program. We discussed how modern Position Independent Executables (PIE) rely on the PLT (Procedure Linkage T...

When you write the absolute simplest C program—one that does nothing but exit successfully—you might expect the compiled output to be trivial. int main() { return 0; } However, executing t...