High-Performance Libri GmbH High-Performance Inference Serving Paperback

Stop burning GPU compute on naive deployments. Transform your models into ultra-low-latency, high-throughput inference engines.

Training a model is only the first step. Serving it in production, handling massive concurrent requests without bankrupting your infrastructure budget or bottlenecking your application is a hardcore systems engineering discipline. Standard Python wrappers and naive API servers collapse under enterprise loads.

High-Performance Inference Serving is the definitive operational manual for architects and MLOps engineers scaling AI in production. We strip away the introductory data science and dive straight into the physics of model serving. You will master the bare-metal realities of the GPU memory wall, KV cache management, and hardware-sympathetic execution required to squeeze maximum throughput from modern accelerators.
>Inside this manual, you will execute:
>State-of-the-Art Compression: Shrinking massive models via PTQ workflows using GPTQ, AWQ, and SmoothQuant to drastically reduce memory bandwidth requirements.

Speculative & Parallel Decoding: Slashing latency with draft models, Medusa, and Lookahead decoding to multiply token generation speed.

Kernel-Level Optimization: Bypassing compiler-generated kernels to implement Operator Fusion and FlashAttention for zero-overhead memory round-trips.

Enterprise Production Architecture: Containerizing inference engines like vLLM, TensorRT-LLM, and Triton, and deploying autoscaling Kubernetes architectures with strict observability SLOs.

Who is this for?
>Stop over-provisioning hardware to compensate for poor architecture. Grab your copy and deploy at scale today.