THE PRACTICAL PLAYBOOK FOR FASTER, CHEAPER LLM INFERENCE: VLLM, KV CACHES, AND DECODING TRICKS
A hands-on deep dive shows how to speed up and scale LLM inference with vLLM, KV caching, and modern attention/decoding optimizations. This new chapter of the ...
A hands-on deep dive shows how to speed up and scale LLM inference with vLLM, KV caching, and modern attention/decoding optimizations.
This new chapter of the LLMOps course walks through prefill vs decode phases, KV caching (plus PagedAttention and prefix caching), attention optimizations like FlashAttention and GQA, speculative decoding, and parallelism strategies, with experiments comparing vLLM to “vanilla” serving read the deep dive.
Two other reads offered less actionable signal: a marketing-style overview of ByteDance’s Monolith recommender infra and a high-level piece on unsupervised graph learning without concrete releases or benchmarks (ByteDance, Tencent).
Inference, not training, usually dominates LLM production cost, latency, and GPU utilization.
Techniques like continuous batching, KV caching, and speculative decoding can unlock throughput and tail-latency wins without retraining.
-
terminal
Benchmark vLLM vs your current server on identical prompts to profile prefill/decode time, context lengths, and tail latency under load.
-
terminal
Enable KV caching (with paged KV) and try speculative decoding to measure throughput and GPU memory swings across short vs long prompts.
Legacy codebase integration strategies...
- 01.
Swap in vLLM behind a canary, keep request/response compatible, and compare P95/P99 and GPU hours per million tokens.
- 02.
Audit long-context traffic; if OOMs or cache thrash happen, test paged KV and prefix caching before scaling hardware.
Fresh architecture paradigms...
- 01.
Design for streaming, continuous batching, and KV cache reuse from day one to avoid architectural rework later.
- 02.
Choose an inference stack that supports FlashAttention/GQA/speculative decoding and plan for model parallelism early.