---
type: source
title: "The HARD Truth About Hosting Your Own LLMs"
description: "Self-hosting powerful open LLMs is only cost-effective at scale, so start by paying per token for the same models via a cheap inference service like Groq and switch to your own cloud GPU only once napkin-math break-even makes it worthwhile."
youtube_id: EMuBqcO048E
url: https://www.youtube.com/watch?v=EMuBqcO048E
slug: the-hard-truth-about-hosting-your-own-llms
published: 2024-09-25
duration: "0:14:45"
recency_rank: 161
raw: "../raw/the-hard-truth-about-hosting-your-own-llms.md"
tags: [local-ai, self-hosting, cost, llms]
created: 2026-07-21
updated: 2026-07-21
---

# The HARD Truth About Hosting Your Own LLMs

Cole confronts the hard truth of running powerful open-weight LLMs like Llama 3.1 yourself: they demand insanely expensive hardware (GPUs costing thousands, plus electricity, or cloud GPU machines at more than a dollar an hour), and the first time you try one locally it will be painfully slow or time out entirely. Local LLMs give you privacy, flexibility, and the best per-token economics at scale, but the upfront cost of getting there is brutal.

His strategy resolves that tension: do not self-host off the bat. Instead pay per token for the exact same self-hostable model through a cheap, fast inference provider (his pick is Groq, which offers roughly 1,200 tokens per second, an indefinite free tier, and drop-in compatibility by swapping the OpenAI base URL, or via LangChain and n8n integrations). Because you are using the same model, you can later migrate to self-hosting without switching LLMs and risking unintended consequences for your application. While on a hosted provider, use mock data for anything truly private, since it is still a third party, then move private workloads once you self-host.

The switch point is calculable. Cole walks a napkin-math example: a RunPod A40 (48GB VRAM, good for Llama 3.1 70B) at $0.39 an hour costs about $280 a month. Groq's Llama 3.1 70B gives roughly 1.69 million tokens per dollar; dividing by an assumed 5,000 tokens per prompt yields prompts per dollar, and multiplying by the $280 gives a monthly break-even of roughly 94,000 prompts (about 3,000 a day). Above that volume, self-hosting on a cloud GPU wins. He recommends cloud GPU (RunPod over the pricier DigitalOcean) rather than a home rig to avoid maintenance, electricity, and hardware-upgrade pain.

## Concepts covered

- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [Agent Deployment](../concepts/agent-deployment.md)
- [Provider Independence](../concepts/provider-independence.md)

## Entities

- [Groq](../entities/organizations/groq.md)
- [Llama](../entities/tools/llama.md)
- [RunPod](../entities/tools/runpod.md)
- [DigitalOcean](../entities/organizations/digital-ocean.md)
- [n8n](../entities/tools/n8n.md)
- [LangChain](../entities/tools/langchain.md)
- [Cole Medin](../entities/people/cole-medin.md)

## Key moments

- **[0:00:36]** The hard truth: powerful local LLMs like Llama 3.1 need insanely powerful hardware and will be slow or time out
- **[0:01:12]** Cost reality: GPUs cost thousands, plus electricity, or cloud GPU machines exceeding a dollar an hour
- **[0:02:02]** The strategy premise: pay per token for the same self-hostable model now, self-host later when the price is right
- **[0:03:00]** Introduces Groq as the cheap, fast pay-per-token inference provider (not sponsored)
- **[0:04:43]** How easy Groq is: swap the OpenAI base URL, add your key, switch the model; also LangChain and n8n
- **[0:06:11]** Groq pricing: Llama 3.1 70B at about $0.59 per million tokens, roughly 1.69M tokens per dollar; compared to Together AI
- **[0:07:07]** Privacy caveat: Groq still hosts the model, so use mock data for very private info until self-hosted
- **[0:08:47]** Cloud GPU options: RunPod (cheaper, an A40 example) vs DigitalOcean (pricier); why cloud over a home rig
- **[0:10:01]** Napkin math begins: A40 at $0.39/hr x 24 x 30 equals about $280 a month
- **[0:12:38]** Break-even: about 94,000 prompts a month (roughly 3,000 a day) is where self-hosting beats paying Groq per token

## Transcript

[Raw transcript](../raw/the-hard-truth-about-hosting-your-own-llms.md)
