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Home > Computing and Information Technology > Computer programming / software engineering > Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More
Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More

Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More


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About the Book

Are you struggling to scale your large language models (LLMs) without breaking the bank or sacrificing latency? This book offers a clear roadmap to optimize inference, reduce costs, and scale seamlessly across platforms like PyTorch, ONNX, vLLM, and more. Optimizing LLM Performance is your hands-on guide to boosting the efficiency of large language models in production environments. Whether you're building chatbots, document summarizers, or enterprise AI tools, this book teaches proven methods to accelerate inference while maintaining accuracy. It dives deep into hardware-aware optimizations, quantization, model pruning, compiler acceleration, and memory-efficient runtime strategies without locking you into any single framework. Written with clarity and real-world use in mind, the book features practical case studies, side-by-side performance comparisons, and up-to-date techniques from the cutting edge of AI deployment. If you're building, serving, or scaling LLMs in 2025, this is the performance engineering guide you've been waiting for. Key Features: - Framework-agnostic optimization techniques using PyTorch, ONNX Runtime, vLLM, llama.cpp, and more - Deep dive into quantization (INT8/4-bit), distillation, pruning, and KV caching - Hands-on examples with FastAPI, Hugging Face Transformers, and serverless deployment - Covers performance profiling, streaming, batching, and cost-efficient scaling - Future-proof insights on compiler-aware models, LoRA 2.0, and edge inference Ready to build LLM systems that are faster, cheaper, and more scalable? Grab your copy of Optimizing LLM Performance today and deploy smarter.


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Product Details
  • ISBN-13: 9798294338459
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 164
  • Returnable: N
  • Sub Title: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More
  • Width: 178 mm
  • ISBN-10: 8294338451
  • Publisher Date: 26 Jul 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 9 mm
  • Weight: 349 gr


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Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More
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