Efficient LLM Inference with Neural Speed

Running large language models efficiently requires optimizing hardware resources without sacrificing output quality. This text-based guide teaches you how to leverage Neural Speed and modern inference techniques to deploy high-performing models on everyday hardware. You will develop a solid understanding of how quantization, memory bandwidth management, and specialized runtime optimizations work together to dramatically reduce latency and compute costs. What you'll learn: - Understand core concepts of large language model architectures and inference bottlenecks - Apply low-bit quantization techniques such as 4-bit and 8-bit weight compression - Configure Neural Speed runtime settings for optimized CPU and GPU execution - Measure and benchmark inference latency, throughput, and memory consumption - Implement prompt caching and kv-cache optimization strategies for continuous generation - Deploy optimized models using lightweight Python scripts and modern inference pipelines The course begins with foundational definitions and key terminology before moving systematically into quantization mechanics, hardware configurations, and practical code walkthroughs. This course is designed for beginners, developers, and AI enthusiasts interested in running local models efficiently. Basic familiarity with Python is helpful, but no prior background in deep learning optimization is required. Start reading today to optimize and accelerate your local language model deployments.