Optimizing LLM Performance: Framework-Agnostic Techniques for Speed, Scalability, and Cost-Efficient Inference Across PyTorch, ONNX, vLLM, and More
暫譯: 優化LLM性能：跨越PyTorch、ONNX、vLLM等的框架無關技術以提升速度、可擴展性和成本效益推理

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.