Learn the frontier · Explainers
From the math down to the kernel.
Evergreen, deeply taught technique walkthroughs. Mechanistic, code-aware, and generous with context — written to go deep enough that you could reimplement the idea yourself, not just nod along. The kind of piece you might bookmark and come back to when you want a thing to truly click.
The headline benchmark is the least durable thing in a model release. Here is how to read access, licenses, cards, eval protocols, and serving facts before you commit engineering to a number you cannot reproduce.
Post-training quantization is the cheapest inference lever and the easiest to pull wrong. The right method is set by your serving regime — bandwidth-bound decode wants weight-only INT4, compute-bound prefill wants FP8 — and the win is real only if a fast kernel accelerates your exact config.
GRPO swaps PPO's learned critic for a Monte-Carlo baseline — the mean reward over a group of sampled completions — trading rollout compute and per-token credit assignment for a simpler, more stable RL loop on verifiable-reward tasks.
MoE decouples parameter count from per-token FLOPs, but every hard problem — instability, dropped tokens, load imbalance, all-to-all traffic, a footprint set by total not active params — lives in the router. A structural tour from Switch/GShard to fine-grained and aux-loss-free designs, and the systems bill you actually pay.
RoPE turns position into a per-dimension rotation — and that same rotation is why PI, NTK-aware scaling, and YaRN exist, and why a 128k window rarely means 128k of usable context. The math, the methods, and the serving bill.
Past one GPU you stop training a model and start operating a distributed system. Here is what each parallelism axis actually shards, what it costs on the wire, and how practitioners stack them into 3D/4D layouts.