vLLM and High-Performance Inference: Memory Optimization, Parallel Execution, Token Streaming, and Scalable Model Serving (Large Language Model Refinement and Inference Series)
Language: English
Published by Independently published, 2026
Series: Book 2 of 2 - Large Language Model Refinement and Inference Series
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
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- Title
- vLLM and High-Performance Inference: Memory Optimization, Parallel Execution, Token Streaming, and Scalable Model Serving (Large Language Model Refinement and Inference Series)
- Author
- Cypher, Camila
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798195860981
- 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.
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California Books
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