Master Ollama - The Speed Playbook: Run Local LLMs 10x Faster and Eliminate Cloud AI Costs This Weekend (Local AI Playbooks Book 2)

You installed Ollama. Pulled a model. Thirteen minutes later, you got a response. You went back to ChatGPT.

Meanwhile, developers on Reddit run 26-billion-parameter models at 300 tokens per second on a Mac Studio. Someone compiled llama.cpp on their hardware and doubled their inference speed overnight. Another pooled a junk drawer of GPUs into one endpoint and got 1.86x the speed of Ollama alone. The difference is not better hardware. The difference is not money. It is configuration.

This is not another install guide. This book makes Ollama run fast. By chapter 3, you will benchmark your GPU and know which models fit. By chapter 7, you will have fixed the speed bottleneck that made you quit. By chapter 9 you will know how to squeeze a 31B model onto a 16GB GPU using quantization techniques most books never mention.

The 10 Ollama books on Amazon stop at installation. None benchmark Gemma 4, DeepSeek R1, or Qwen 3.6 on real hardware. None test Ollama vs llama.cpp vs LM Studio head-to-head. This is what those books left out.

What you will walk away with: Benchmark your GPU speed in 15 minutes to know exactly which models fit. Settle Ollama vs llama.cpp with measured tok/s on your machine. Go from 3 tok/s to 30+ by fixing the one setting most setups get wrong. Predict any model's VRAM fit in 30 seconds: stop downloading models that crash. Three Modelfiles that make the same model produce visibly better output. Build a 24/7 AI server from a $0 spare phone or $300 mini-PC. Save $200/month with a one-page local-vs-cloud decision sheet. Covers Gemma 4, Qwen 3.6, DeepSeek & more. Most Ollama books on Amazon stop at ollama run. This is the 200+ pages that come after.