Every team that serves language models eventually hits the same wall: the GPU has plenty of memory and the model
Continue readingContinuous Batching: The Scheduling Trick Behind Every Fast LLM Server
Every LLM application has two knobs that get blamed for everything: temperature and top-p. When outputs are boring, someone raises
Continue readingTemperature, Top-p, Top-k, and Min-p: How the LLM Sampler Actually Works
Four-bit weights used to be a hobbyist compromise — the thing you did to squeeze a 70B model onto a
Continue reading4-Bit by Default: Choosing Between GPTQ, AWQ, GGUF, and MXFP4 in 2026
Ask someone to sketch a transformer on a whiteboard and they’ll draw the 2017 version: softmax attention over every pair
Continue readingMHA, MQA, GQA, MLA: The KV-Cache Trade-offs Behind Every Modern Transformer
Most teams that ship a retrieval-augmented generation feature never measure it. The demo looks good, a few hand-picked questions return
Continue readingStop Vibing Your RAG Pipeline: Recall@k, MRR, and NDCG in an Afternoon
Run two unrelated documents through a large language model at the same time, and you assume the model keeps them
Continue readingYour Transformer Can Hold Two Thoughts at Once: The New Linear Superposition Result
Another week, another pile of repositories crossing the ten-thousand-star mark in days. Most weekly roundups stop at the star count,
Continue readingTrending GitHub Repos This Week: Typed Decision Models Take Over the Trending Page
DeepSeeded? No — DeepSeek. In September 2026 the Chinese AI lab released a paper titled “DeepSeek-V4.1-Flash: Pushing the Limits of
Serving a large language model to thousands of concurrent users is, underneath all the marketing, a memory management problem. Every
Continue readingDeepSeek-V4.1-Flash: Why the Most Interesting AI Paper This Month Is About Storage
Autoregressive decoding is the reason large language models feel slow: every token is generated by a full forward pass, and