High-Performance Inference Serving: Batching, Quantization, and Low-Latency Model Deployment.

By Denton Malcom and Elmer Robinson

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.
This playbook bridges the gap between model weights and enterprise infrastructure. From deploying state-of-the-art quantization techniques to orchestrating continuous batching and speculative decoding, this book provides the exact blueprints to deploy LLMs and multi-modal models at scale.

Inside this manual, you will execute:
Advanced Batching & Paged Memory: Treating GPU VRAM like virtual memory, deploying PagedAttention, and implementing continuous batching to maximize hardware utilization.

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?
This manual is engineered exclusively for Senior MLOps Engineers, AI Infrastructure Architects, Backend Systems Programmers, and Technical Leads. If you are responsible for lowering inference costs and crushing latency bottlenecks in production, this is your blueprint.

Stop over-provisioning hardware to compensate for poor architecture. Grab your copy and deploy at scale today.

• Format: Paperback
• Language: English
• ISBN: B0H9P85QKK
• ISBN13: 9798188217280
• Release Date: July 2026
• Publisher: Independently Published
• Length: 400 Pages
• Weight: 1.40 lbs.
• Dimensions: 0.8" x 6.7" x 9.6"