What Kimi K3 asks of the hardware
Reading the Kimi K3 announcement from the systems side. How linear attention changes the KV-cache budget, what 896 experts mean for the interconnect, and why the model ships in MXFP4.
dwarezMachine learning systems, from the metal up
Notes for people who like opening the black box.
Low-level ML explanations, implementation notes, debugging stories, and the occasional sharp edge worth remembering.
GGUF quantization, bit by bit
Q8_0 to IQ1_S and everything in between. Block layouts, scale hierarchies, codebooks, the importance matrix, and the mixed recipes used by llama.cpp, Unsloth, and DwarfStar.
GRPO is mostly a systems problem
The GRPO config is small. The hard part is keeping rollout workers, trainers, rewards, and policy versions in the same story.
A fused Lion optimizer kernel with Hugging Face kernels
I wrote a fused Lion optimizer step with Hugging Face kernels, packaged it with kernel-builder, and tested it on CUDA and Metal/MPS.
TurboQuant: What 3-Bit KV Caches Actually Mean for Your Inference Stack
A quick look to Google's new quantization method, with a bit of drama
Transformer Attention, Backwards
You already know what a language model does. Let's reverse-engineer how.
Pipeline Parallelism: Surgery on Models Too Big for the Operating Table
In our latest article, we explored Data Parallelism.
Data Parallelism: Scaling LLM Training Through Parallel Processing
In my latest article , I discussed the theoretical memory usage needed for inference and training with LLMs, highlighting the memory cost of each component involved in the process.
The Memory Anatomy of Large Language Models: A Surgeon's Guide
Picture this: you’re about to deploy a shiny new 70-billion parameter language model, and your colleague asks the dreaded question: “How much GPU memory do we need?”.
From Sequential to Parallel: Your Journey into GPU Programming with Triton
We all know that GPU programming is hard.
The Transformer's Anatomy: A Deep Dive into the Architecture that Revolutionized Machine Learning
In the vast landscape of machine learning, few architectures have captured the imagination and transformed the field as profoundly as the Transformer. Like a master anatomist approaching a complex organism, we must carefully dissect each component to understand how this remarkable architecture breathes life into modern AI systems.
Move Fast or Die Slow
Today’s article steps back from our usual technical deep-dives to examine the strategic importance of ML optimization.
The Machine Learning Surgeon's Guide to Quantization: Precision Cuts for Smarter Models
As humans, we perceive space and time as a seamless, continuous flow.
The Operating Room Setup
Undoubtedly, one of the most critical aspects of machine learning is understanding the theory—without grasping how machines learn, you’ll never excel as an ML Surgeon!
Dissecting torch.compile: Surgical Precision in PyTorch Optimization
You can take a look at the GitHub repository of this blogpost at this link
A quick incision: ten minutes to RAG
In under 10 minutes, you’ll discover what RAG is, how to build a prototype for it in Python, and—most importantly—the true weight of the infamous Mr. Fat Raccoon.
Performing Kernel Surgery: Profiling CUDA Kernels with NVIDIA Nsight Compute
Being a Machine Learning Surgeon is not an easy life.
A Machine Learning Surgeon’s Toolkit: Advanced Matrix Multiplication in CUDA
If you want to learn how to write a CUDA kernel for matrix multiplication, look no further!
Cerebral Cortex and Hippocampus: Understanding the Computational and Memory Design of GPUs
Just as a surgeon needs to understand anatomy, knowing GPU architecture is key to writing efficient kernels. You don’t need to be a hardware expert—just grasp the basics to unlock their full potential
Hello CUDA: A Surgical Dissection
CUDA enables developers to harness NVIDIA GPUs for general-purpose tasks. This article guides you to a "Hello, World!" program as a starting point.
An Introduction to Sparsity for Efficient Neural Network Inference
Sparsity is a solution to reduce the number of parameters and number of operations in Neural Networks, granting outstanding computational speedups and memory savings during inference.