{"product_id":"the-local-ai-performance-handbook-optimizing-ollama-for-multi-gpu-and-hardware-acceleration-9798195802172","title":"The Local AI Performance Handbook: Optimizing Ollama for Multi-GPU and Hardware Acceleration","description":"\u003cp\u003e • Author(s): Ethan Tyson\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Expert Systems\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe Local AI Performance Handbook: Optimizing Ollama for Multi-GPU and Hardware Acceleration\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eLocal AI is powerful, but poor configuration can turn expensive hardware into a slow, unstable bottleneck. If your Ollama setup struggles with VRAM limits, weak token throughput, GPU underuse, long context slowdowns, or unreliable multi-user workloads, this handbook gives you the practical performance playbook you need.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe Local AI Performance Handbook\u003c\/b\u003e is a technical guide to building faster, more private, and more reliable Ollama systems across NVIDIA CUDA, AMD ROCm, Apple Silicon, WSL2, Docker, Kubernetes, and multi-GPU environments. It moves beyond basic local model setup and focuses on the engineering details that determine real-world performance: hardware acceleration, VRAM planning, quantization, request concurrency, private RAG, secure deployment, benchmarking, and production maintenance. The book's scope is reflected in its coverage of hardware-specific runtimes, memory engineering, multi-GPU scheduling, quantization, high-concurrency handling, private RAG, deployment, agentic workflows, and troubleshooting.\u003c\/p\u003e\u003cp\u003eInside, readers will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eConfigure Ollama for CUDA, ROCm, Apple Silicon, Vulkan, Docker, and WSL2.\u003c\/li\u003e\n\u003cli\u003eCalculate model memory footprints and avoid out-of-memory failures.\u003c\/li\u003e\n\u003cli\u003eTune VRAM usage, KV cache behavior, context windows, and quantization choices.\u003c\/li\u003e\n\u003cli\u003eScale Ollama across multiple GPUs and isolate workloads with resource controls.\u003c\/li\u003e\n\u003cli\u003eBenchmark tokens per second, latency, GPU utilization, and system bottlenecks.\u003c\/li\u003e\n\u003cli\u003eDeploy private AI inference with Docker Compose, Kubernetes, health checks, and secure API access.\u003c\/li\u003e\n\u003cli\u003eBuild faster private RAG and local agent workflows without depending on cloud APIs.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eFor developers, AI engineers, homelab builders, and technical teams serious about private AI performance, this book turns Ollama from a simple local model runner into a tuned inference platform.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47882608148631,"sku":"9798195802172","price":2103.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798195802172.webp?v=1781096209","url":"https:\/\/atlanticbooks.com\/products\/the-local-ai-performance-handbook-optimizing-ollama-for-multi-gpu-and-hardware-acceleration-9798195802172","provider":"Atlantic Books","version":"1.0","type":"link"}