A series index for LLM attention kernels and GPU primitives: fused softmax, online softmax, FlashAttention, PagedAttention kernels, Triton/CUDA, and memory-access optimization.

A series index for LLM quantization and low-precision serving: INT8/INT4, GPTQ, AWQ, SmoothQuant, NF4, AQLM, KV cache quantization, FP8 serving, and quality/speed/memory tradeoffs.

An LLM inference experiment series index: vLLM/SGLang benchmarks, TTFT/TPOT, prefix cache, chunked prefill, PagedAttention, quantization, and a profiler dashboard.

A vLLM / SGLang source-reading series index: request lifecycle, scheduler, KV cache allocation, block manager, radix cache, and benchmarks.

A series index for core LLM serving mechanisms: prefill/decode, KV cache, PagedAttention, continuous batching, prefix caching, and disaggregated prefill.

A practical survey of LLM quantization, covering linear quantization, codebook quantization, LLM.int8(), SmoothQuant, GPTQ, AWQ, NF4, AQLM, KV cache quantization, and FP8.

A step-by-step explanation of positional encoding in Transformers, from absolute embeddings to sinusoidal encodings, Euler's formula, and rotary position embeddings.

A back-of-the-envelope framework for estimating LLM training FLOPs, inference FLOPs, weight memory, KV cache, and training memory.

A practical note on managing agent skills: how to install, create, remove, disable, update, and evolve skills, plus which meta-skills are worth installing first.

Routing prefill and decode to separate GPU pools eliminates interference entirely, enabling independent scaling and optimal latency — at the cost of KV cache migration across machines.