vLLM and High-Performance Inference : Memory Optimization, Parallel Execution, Token Streaming, and Scalable Model Serving
Language: English
Published by Amazon Digital Services LLC - Kdp Mai 2026, 2026
Series: Book 2 of 2 - Large Language Model Refinement and Inference Series
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 32.55
Quantity: 2 available
Add to basketItem description from seller
Neuware - Once a language model has been refined, its effectiveness depends on how well it can be delivered in real-world environments. This book examines the systems and techniques that enable efficient inference, with a particular focus on vLLM and the architectural decisions that support high-throughput execution.The text begins by establishing the relationship between model size, hardware constraints, and response latency. It then explores how memory is managed during inference, including strategies that reduce overhead while maintaining output quality. Concepts such as batching, caching, and token-level scheduling are presented in a way that reveals their practical impact on performance.A central theme of the book is parallel execution, where multiple requests are handled simultaneously without degrading responsiveness. The discussion highlights how modern inference frameworks distribute workloads, coordinate computation, and maintain consistency across concurrent processes.Token streaming is examined as a critical component of user-facing systems, showing how incremental output generation improves perceived responsiveness and interaction flow. The material connects these techniques to broader system considerations, including scaling across machines, managing resource allocation, and maintaining stability under load.As the book progresses, it presents a unified view of inference as both a technical and operational challenge. It demonstrates how decisions made at the system level directly influence user experience, cost efficiency, and reliability.By the end, readers will have a clear understanding of how optimized inference transforms a refined model into a responsive and scalable system capable of operating under demanding conditions.…
Seller Inventory # 9798195860981
- Title
- vLLM and High-Performance Inference : Memory Optimization, Parallel Execution, Token Streaming, and Scalable Model Serving
- Author
- Camila Cypher
- Publisher
- Amazon Digital Services LLC - Kdp Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798195860981
- Item weight
- 331 grams
- Dimensions
- 254x178x10 mm
- Series
- Book 2 of 2: Large Language Model Refinement and Inference Series
The text begins by establishing the relationship between model size, hardware constraints, and response latency. It then explores how memory is managed during inference, including strategies that reduce overhead while maintaining output quality. Concepts such as batching, caching, and token-level scheduling are presented in a way that reveals their practical impact on performance.
A central theme of the book is parallel execution, where multiple requests are handled simultaneously without degrading responsiveness. The discussion highlights how modern inference frameworks distribute workloads, coordinate computation, and maintain consistency across concurrent processes.
Token streaming is examined as a critical component of user-facing systems, showing how incremental output generation improves perceived responsiveness and interaction flow. The material connects these techniques to broader system considerations, including scaling across machines, managing resource allocation, and maintaining stability under load.
As the book progresses, it presents a unified view of inference as both a technical and operational challenge. It demonstrates how decisions made at the system level directly influence user experience, cost efficiency, and reliability.
By the end, readers will have a clear understanding of how optimized inference transforms a refined model into a responsive and scalable system capable of operating under demanding conditions.
"Synopsis" may belong to another edition of this title.
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