Performance

A globe overlaid with edge node connections, symbolizing Cloudflare's global network.

Cloudflare Zero to Hero: A Working Engineer's Guide to Building on the Edge

From your first Worker to a globally distributed application: a practical walkthrough of Cloudflare’s edge stack, the patterns that work in production, and the trade-offs you should know before going all-in.

Two conveyor belts running at different speeds, one fast and one slow, photographed from above.

Throughput vs Latency: Why Optimizing One Will Almost Always Break the Other

Throughput and latency measure different things, and most of the systems pain we feel at scale comes from pretending they don’t. Here is how they actually trade off, with real production patterns.

Two pipelines merging — a fast draft model feeding into a verifier that accepts or rejects proposed tokens.

Speculative Decoding: How Production LLM Systems Cut Latency by 2–3x

Speculative decoding trades a small draft model for big latency wins — 2–3x faster token generation with mathematically identical outputs. Here’s how it works and where production systems use it.

Binary digits flowing into a neural network diagram representing reduced precision inference.

Quantization in Production: Trading Precision for Scale

Quantization is the single highest-leverage optimization for LLM and vision inference at scale. This post covers the math, the formats, the failure modes, and the patterns shipping teams use to roll out quantized models safely.

Diagram of V8's TurboFan pipeline showing bytecode, IR graphs, and machine code stages.

Inside V8's TurboFan Optimizing Pipeline: Tracing JIT Compilation from Bytecode to Machine Code

Trace a JavaScript function from parsed bytecode all the way through TurboFan’s Sea of Nodes IR, speculative optimizations, and final machine code emission, with the concrete phase ordering V8 actually uses.