A hands-on build guide for a blockwise INT4 inference engine with packed-nibble weights, per-group scales/zero-points, and fused dequantizing GEMM — a portfolio project that demonstrates real systems and ML engineering depth.
A hands-on guide to building a minimal LLM inference engine with paged attention and adaptive KV-cache eviction in pure Python, designed to demonstrate real systems engineering prowess on your CV.
Ollama is an open-source framework that enables running large language models (LLMs) locally on personal hardware, prioritizing privacy, low latency, and ease of use.[1][2] At its core, Ollama leverages llama.cpp as its inference engine within a client-server architecture, packaging models like Llama for seamless local execution without cloud dependencies.[2][3] This comprehensive guide dissects Ollama’s internal mechanics, from model management to inference pipelines, quantization techniques, and hardware optimization. Whether you’re a developer integrating Ollama into apps or a curious engineer, you’ll gain actionable insights into its layered design. ...