Blueprint · advanced · 10 steps

Deploy Your Own Open-Source LLM

Run Llama 3.1 locally with Ollama, benchmark it, then deploy a production API with vLLM and Docker.

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STEP 01Step 1: What We're BuildingDeploy an open-source LLM locally with Ollama — pull a model, run it, call it from Python, benchmark it, and customize it with Modelfiles.3 min→STEP 02Step 2: Install OllamaInstall Ollama on your machine and verify it is running — the simplest way to run open-source LLMs locally.3 min→STEP 03Step 3: Run Your First ModelPull Llama 3.1 8B, run an interactive chat session, and understand how tokens, context windows, and model loading work.3 min→STEP 04Step 4: Ollama APIUse Ollama's OpenAI-compatible REST API from curl and Python to integrate your local LLM into real applications.3 min→STEP 05Step 5: QuantizationUnderstand how quantization shrinks model sizes by 2-4x while preserving most quality — and compare Q4, Q8, and FP16 variants hands-on.3 min→STEP 06Step 6: Benchmark ModelsWrite a Python benchmarking script that measures tokens per second, time to first token, and total latency — giving you real performance data for your hardware.4 min→STEP 07Step 7: Customize with ModelfilesCreate custom models using Ollama's Modelfile system — set system prompts, adjust parameters, and build specialized models for different use cases.4 min→STEP 08Step 8: What's NextExplore fine-tuning with LoRA, production serving with vLLM, and a cost comparison showing when self-hosting beats API providers.3 min→