How transformer inference actually works under the hood — and why KV cache is the single most...
How transformer inference actually works under the hood — and why KV cache is the single most important optimization keeping your LLM from crawling.
If you've ever wondered why LLMs respond fast even on long prompts — the answer is KV cache. But most explanations stop at "it stores keys and values." This goes deeper.
What You'll Learn
By the end of this article you'll understand:
Why autoregressive LLM generation is expensive by design
Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
Consistency diffusion language models: Up to 14x faster inference without sacrificing quality
AI 101: From Tokens to Answers: What Actually Happens During LLM Inference
Cache-aware prefill–decode disaggregation (CPD) for up to 40% faster long-context LLM serving
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
FP8 and INT8 KV caches cut attention state ~50%, but they shift the target model's logit distribution — and that can quietly…
Why Agent Workloads Are Expensive LLM inference costs always scale with context length. In...
LLM KV Cache Optimization, Open Model Evaluation, & Agent Engineering Skills for Local...
RAG has become the default answer for giving LLMs access to private knowledge. And for good reason —...
LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by…
