CPU Inference with BitNet.cpp and Modern MLOps

Running large language models often demands costly, specialized hardware, creating massive infrastructure hurdles for developers and teams. BitNet.cpp changes this paradigm by enabling fast, efficient 1-bit and ternary model inference directly on standard CPU architectures. This text-based course guides you through the foundational concepts of extreme model quantization, local CPU inference pipelines, and essential MLOps deployment practices. You will gain a solid technical understanding of how ternary weight architectures function and how to orchestrate lightweight, scalable model serving without dedicated accelerators. What you'll learn: - Understand the core principles of extreme quantization and 1.58-bit model architectures - Configure and build BitNet.cpp environments for local and server-side CPU inference - Deploy large language models efficiently across commodity hardware - Integrate modern MLOps practices for model lifecycle tracking and environment consistency - Implement lightweight API endpoints to serve quantized models in production - Measure inference latency, memory consumption, and throughput metrics You will begin with essential terminology and the mathematical intuition behind quantized weights before progressing step by step through configuration files, setup guides, and practical written exercises. This course is designed for software developers, data practitioners, and technical enthusiasts interested in cost-effective AI deployment. No prior experience with extreme quantization or specialized hardware is required. Start reading today to run modern language models efficiently on standard CPUs.