Serving LLMs in Production
GPUs, batching, and the latency budget you forgot about
Notebook demos do not survive contact with production traffic. This book covers GPU memory math, continuous batching, paged attention, KV cache eviction, speculative decoding, and the cost models that determine whether a feature ships green or red. Includes load test results across vLLM, TGI, and TensorRT-LLM at three traffic shapes.
Priya runs the kind of ML platforms where a 200ms regression costs more than your annual cloud bill. Her writing focuses on the boring infrastructure that makes models actually serve traffic.
- Pages
- 384
- Edition
- 1st Edition
- Language
- English
- Level
- advanced
- ISBN
- 978-1-99999-009-8
- Published
- March 2026
Reviewed by three working engineers at peer publications before publication. We do not publish first drafts.
What you'll find inside.
- 01The Latency Budget
- 02GPU Memory Math, Honestly
- 03Continuous Batching Demystified
- 04KV Cache Eviction Strategies
- 05Speculative Decoding
- 06Quantization Without Regret
- 07Multi-tenant Serving
- 08The Cost Model That Convinces Finance
- 09Observability for Token Streams
5.0 / 5
274 verified readers
Numbers, not vibes
Every other LLM serving guide I read was a vendor pitch in disguise. This one gives real load test data. The GPU math chapter is gold.
