---
type: entity
subtype: tool
title: "RunPod"
description: "GPU cloud recommended when you need a powerful high-VRAM machine to run large language models like Llama 3.2 90B."
resource: "https://www.runpod.io"
tags: [gpu-cloud, local-ai, deployment]
videos: [revealing-my-complete-ai-agent-blueprint, deploy-any-ai-app-to-the-cloud-fast, the-hard-truth-about-hosting-your-own-llms]
created: 2026-07-21
updated: 2026-07-21
---

# RunPod

RunPod is the GPU cloud Cole recommends whenever a project needs real compute to run large language models rather than calling a hosted API. When you want to self-host something big, he says, "you're probably going to want a machine from a service like RunPod which is what I'd recommend for that they have a lot of different options to get very powerful machines with a lot of vram to run those larger models" ([0:03:10](../../sources/deploy-any-ai-app-to-the-cloud-fast.md)). The recurring reason he lands on it is price: "runpod has the best pricing for GPU instances that I've seen in general" ([0:10:48](../../sources/revealing-my-complete-ai-agent-blueprint.md)), a point he repeats when weighing the economics of hosting your own LLMs, where "runpod actually has much more competitive pricing you can just kind of look at some comparisons here for like an h100" ([0:09:14](../../sources/the-hard-truth-about-hosting-your-own-llms.md)).

RunPod is the compute half of Cole's [local and self-hosted AI](../../concepts/local-ai.md) story. When a model like Llama 3.2 90B is too large for a laptop, you rent a high-VRAM RunPod instance and run it there, keeping the [provider independence](../../concepts/provider-independence.md) of open weights without owning the hardware. This makes it the natural pairing with the [model selection](../../concepts/model-selection.md) decision: once you have chosen to run an open model, RunPod is where the weights actually live. He typically deploys the workload as a [Docker](./docker.md) container onto the rented GPU, so the same packaging discipline he uses everywhere carries over to accelerated hosting.

Positioned against his other infrastructure picks, RunPod occupies the heavy end. [Render](./render.md) hosts lightweight container agents that call external model APIs; RunPod is where you go when the model itself is the workload and it needs a GPU. His framing is consistently cost-driven: it is the option that makes serious self-hosted inference affordable.

## Realizes

- [Cloud GPU Hosting](../../concepts/cloud-gpu-hosting.md) - Renting GPU machines in the cloud (VRAM-sized to the model, e.g. a 48GB A40 for Llama 3.1 70B) to self-host models without owning, powering, maintaining, or upgrading hardware.
- [Local & Self-Hosted AI](../../concepts/local-ai.md) - Running open-weight models on your own hardware or cloud for privacy, cost control, and independence from API providers.

## Works with

- [Docker](./docker.md) - Containerization prerequisite; a single docker compose command spins up Archon's four containers (agents, MCP server, UI, API server).

## Contrasts with

- [Render](./render.md) - The cloud deployment platform (used on its free tier) that auto-builds and deploys staging on push and production on commit to main.

## Related

- [Model Selection](../../concepts/model-selection.md) - Choosing the right LLM per task by trading off quality, speed, and cost, often routing easy requests to cheap models and hard ones to expensive models.
- [Model Quantization](../../concepts/model-quantization.md) - Compressing model weights to lower precision so large models fit and run on limited local hardware.
- [Provider Independence](../../concepts/provider-independence.md) - Architecting agents so the underlying model, framework, or client can be swapped without a rewrite, avoiding lock-in through low-level abstractions.
- [Agent Deployment](../../concepts/agent-deployment.md) - Getting agents to production, containerizing, networking, reverse-proxying, and choosing a provider-agnostic, cost-predictable host.
- [Agent Development Lifecycle](../../concepts/agent-development-lifecycle.md) - A repeatable blueprint for taking an agent from planning and a minimal proof-of-concept through database, code, UI, testing, deployment, and evaluation.
- [Retrieval-Augmented Generation (RAG)](../../concepts/rag.md) - Grounding an LLM's answers by retrieving relevant documents from an external knowledge base and injecting them into the prompt at query time.
- [Ollama](./ollama.md) - Local LLM runtime; works with a locally-run bolt.diy but can't be reached by a self-hosted (remote) instance.

## Sources

- [Deploy ANY AI App to the Cloud FAST](../../sources/deploy-any-ai-app-to-the-cloud-fast.md) - "[0:03:10] you're probably going to want a machine from a service like RunPod which is what I'd recommend for that they have a lot of different options to get very powerful machines with a lot of vram to run those larger models"
- [Revealing my COMPLETE AI Agent Blueprint](../../sources/revealing-my-complete-ai-agent-blueprint.md) - "[0:10:48] runpod has the best pricing for GPU instances that I've seen in general"
- [The HARD Truth About Hosting Your Own LLMs](../../sources/the-hard-truth-about-hosting-your-own-llms.md) - "[0:09:14] runpod actually has much more competitive pricing you can just kind of look at some comparisons here for like an h100"
