GPTQ in Production: Quantize, Validate, and Serve Efficient LLMs

"GPTQ in Production: Quantize, Validate, and Serve Efficient LLMs"

Large language model deployment is no longer limited by model quality alone; it is constrained by memory budgets, runtime behavior, hardware fit, and operational risk. This book is written for experienced ML engineers, platform engineers, and inference specialists who need to move beyond toy quantization demos and make GPTQ work reliably in real systems. It treats GPTQ not as a buzzword, but as a production decision with measurable consequences.

Across the book, readers learn how GPTQ works, where it fits among other compression strategies, and how calibration data, hyperparameter choices, kernels, and serving backends shape final outcomes. The material covers artifact compatibility, Hugging Face and vLLM workflows, validation gates against FP16/BF16 baselines, realistic benchmarking, capacity planning, and rollout safety. By the end, readers will be able to choose sound configurations, build trustworthy evaluation pipelines, and serve quantized LLMs with confidence.

Rather than repeating high-level theory, the book focuses on engineering depth: runtime-specific tradeoffs, version-sensitive ecosystem guidance, failure modes hidden by average metrics, and operational practices for observability, rollback, and incident response. It assumes familiarity with transformer inference, GPU serving, and modern LLM tooling, and rewards readers who want practical, ecosystem-aware guidance for deploying efficient models under production constraints.

© 2026 NobleTrex Press (Libro electrónico): 6610001216764

Fecha de lanzamiento

Libro electrónico: 6 de mayo de 2026