Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
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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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Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More
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Paperback. Condition: new. Paperback. 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 inferenceReady to build LLM systems that are faster, cheaper, and more scalable?Grab your copy of Optimizing LLM Performance today and deploy smarter. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798294338459
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Paperback. Condition: new. Paperback. 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 inferenceReady to build LLM systems that are faster, cheaper, and more scalable?Grab your copy of Optimizing LLM Performance today and deploy smarter. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798294338459
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