---
type: source
title: "The Ultimate Guide to Local AI and AI Agents (The Future is Here)"
description: "You can run large language models and their entire supporting infrastructure entirely on your own hardware to build 100% private, offline AI agents, and Cole's curated 'local AI package' makes standing up that full stack (and deploying it securely to the cloud) achievable step by step."
youtube_id: mNcXue7X8H0
url: https://www.youtube.com/watch?v=mNcXue7X8H0
slug: the-ultimate-guide-to-local-ai-and-ai-agents-the-future-is-here
published: 2025-06-11
duration: ""
recency_rank: 85
raw: "../raw/the-ultimate-guide-to-local-ai-and-ai-agents-the-future-is-here.md"
tags: [local-ai, ollama, quantization, containerization, deployment]
created: 2026-07-21
updated: 2026-07-21
---

# The Ultimate Guide to Local AI and AI Agents (The Future is Here)

This masterclass is an end-to-end guide to local AI: running open-source LLMs and all supporting infrastructure (database, UI, web search, observability) on hardware you control instead of paying for hosted APIs. Cole opens with a hands-on Ollama demo, then argues the case for local AI (privacy/security for regulated industries, fine-tuning, cost-effectiveness, and lower latency) while honestly conceding cloud AI's current advantages (easier setup, less maintenance, stronger frontier models, built-in features), all of which he predicts will erode over time. He then dives deep into the "tricky stuff" that intimidates newcomers: hardware/VRAM requirements by model size (7B, 14B, 32B, 70B), quantization (defaulting to Q4 as the sweet spot), offloading layers across GPU/CPU/RAM, and critical Ollama environment variables (flash attention, context quantization, and especially overriding the tiny 2,000-token default context length).

The practical backbone is OpenAI API compatibility: because Ollama and nearly every provider implement the chat-completions standard, you can swap an existing agent from OpenAI to a local LLM by changing just the base URL and API key. Cole demonstrates this with a Python script, then introduces the "local AI package", a curated Docker Compose stack bundling n8n, Supabase, Ollama, Open Web UI, Flowise, Qdrant, Neo4j, SearXNG, Caddy, and Langfuse, and walks through configuring its environment variables and starting it with GPU/CPU profiles.

He then builds the same agent twice, once no-code in n8n and once in code with Pydantic AI/FastAPI, connecting both to Open Web UI, Supabase (Postgres) for memory, and SearXNG for private web search. A recurring, heavily emphasized lesson is Docker networking: inside the stack you reference containers by service name (e.g. "ollama", "searxng"), use host.docker.internal to reach the host, and localhost only outside containers. Finally he deploys the whole stack plus the containerized Python agent to a Digital Ocean GPU droplet, setting up a firewall, DNS A records, and Caddy as a reverse proxy for automatic HTTPS subdomains, keeping Ollama and SearXNG internal-only for security.

## Concepts covered

- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [Provider Independence](../concepts/provider-independence.md)
- [Model Quantization](../concepts/model-quantization.md)
- [Model Selection](../concepts/model-selection.md)
- [Fine-Tuning](../concepts/fine-tuning.md)
- [Context Window Limits](../concepts/context-window-limits.md)
- [Containerization & Docker Networking](../concepts/containerization.md)
- [Memory Systems](../concepts/memory-systems.md)
- [Reverse Proxy](../concepts/reverse-proxy.md)

## Entities

- [Ollama](../entities/tools/ollama.md)
- [Local AI Package](../entities/tools/local-ai-package.md)
- [n8n](../entities/tools/n8n.md)
- [Supabase](../entities/tools/supabase.md)
- [Open Web UI](../entities/tools/open-web-ui.md)
- [SearXNG](../entities/tools/searxng.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [Caddy](../entities/tools/caddy.md)
- [Docker](../entities/tools/docker.md)
- [Digital Ocean](../entities/organizations/digital-ocean.md)
- [OpenRouter](../entities/tools/openrouter.md)
- [DeepSeek R1](../entities/tools/deepseek-r1.md)
- [Cole Medin](../entities/people/cole-medin.md)
- [Dynamous](../entities/organizations/dynamous.md)

## Key moments

- **[0:02:00]** Defines local AI: running your own LLMs and infrastructure (database, UI) entirely offline on your own machine
- **[0:04:00]** Hands-on Ollama demo, download, ollama run deepseek-r1:1.5b, chat in the terminal
- **[0:07:00]** Pros and cons of local AI vs cloud AI; privacy/security as the headline advantage
- **[0:12:00]** Hardware requirements: VRAM needs and tokens/sec across 7B / 14B / 32B / 70B parameter tiers
- **[0:25:00]** Quantization explained, lowering precision to fit GPUs; Q4 as the recommended sweet spot
- **[0:32:00]** Offloading, splitting model layers between GPU, CPU/RAM, and even storage
- **[0:35:00]** Critical Ollama environment variables: flash attention, KV-cache quantization, and overriding the 2,000-token context default
- **[0:42:00]** OpenAI API compatibility, swap providers by changing only the base URL and API key; Python demo
- **[0:53:00]** Introduces the local AI package: a curated Docker Compose stack of self-hosted services
- **[1:05:00]** Builds a local agent in n8n with Ollama chat model, Supabase memory, and a SearXNG web-search tool wired to Open Web UI
- **[1:18:00]** Rebuilds the same agent in Python with Pydantic AI + FastAPI, then containerizes it into the stack
- **[1:28:00]** Deploys everything to a Digital Ocean GPU droplet with firewall, DNS A records, and Caddy reverse proxy for HTTPS

## Transcript

[Raw transcript](../raw/the-ultimate-guide-to-local-ai-and-ai-agents-the-future-is-here.md)
