GPU Kernel Engineering Optimization

<!-- SC_OFF --><div class="md"><p>The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. I built

<!-- SC_OFF --><div class="md"><p><strong>AI Engineering Topics Covered</strong><br /> Foundation Models<br /> Model as a Service (MaaS)<br /> Scaling Laws<br /> Self-Supervised

<!-- SC_OFF --><div class="md"><p>That foodtruck bench post showing deepseek v4 matching gpt-5.2 at 17x cheaper got me thinking. if frontier cloud models are that overpriced for

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t4uj9h/mtp_on_strix_halo_with_llamacpp_pr_22673/"> <img alt="MTP on strix halo with llama.cpp (PR #22673)"

<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1sez79x/towards_a_bitter_lesson_of_optimization_when/"> <img alt="Towards a Bitter Lesson of Optimization:

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1s7p0au/tracer_learntodefer_for_llm_classification_with/"> <img alt="TRACER: Learn-to-Defer for LLM

<!-- SC_OFF --><div class="md"><p><a