The KV Cache: Why LLM Serving Is a Memory Problem
Every LLM serving stack lives or dies by one data structure most developers never see: the KV cache. It is
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Tag: Quantization
The Definitive Guide to Local LLM Inference on Mac: Why Your Machine Is Slow and the Six Fixes That Work
Every Mac owner who has run a local LLM knows the feeling: the model downloads fine, the first answer looks
4-Bit by Default: Choosing Between GPTQ, AWQ, GGUF, and MXFP4 in 2026
Four-bit weights used to be a hobbyist compromise — the thing you did to squeeze a 70B model onto a
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DeepSeek V4.1-Flash, Explained Like You’re New: KV Caches, MoE, and the 890-Byte Trick
DeepSeeded? No — DeepSeek. In September 2026 the Chinese AI lab released a paper titled “DeepSeek-V4.1-Flash: Pushing the Limits of
LLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
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Training-Inference Mismatch: Why Your LLM Reinforcement Learning Is Optimizing the Wrong Policy
Reinforcement learning has become the defining ingredient of modern LLM post-training. GRPO, PPO, and their variants drive the reasoning capabilities
vLLM v0.23.0: Model Runner V2, Multi-Tier KV Offloading, and the Growing Rust Frontend
The vLLM v0.23.0 release landed last week with 408 commits from 200 contributors, and it packs several changes that directly
The AI Glossary: Every Term You Need to Know in 2026
Entering the AI space feels like learning a new language. Everyone throws around RAG, RLHF, GGUF, MoE, MCP like you’re
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