---
type: concept
title: "Cloud GPU Hosting"
description: "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."
tags: [infrastructure, gpu, self-hosting]
videos: [the-hard-truth-about-hosting-your-own-llms]
created: 2026-07-21
updated: 2026-07-21
---

# Cloud GPU Hosting

Cloud GPU hosting is renting an accelerated machine by the hour and running your own open-weight model on it, instead of either calling a hosted model API or buying a GPU and putting it under your desk. It is the middle path in self-hosting: you still control the weights, the runtime, and the data, but the silicon is someone else's problem. Cole's argument for it is entirely about the ownership overhead you shed: "you have to deal with maintenance paying for electricity it's harder to upgrade when the next level of gpus come out... so generally it's a lot more flexible to just pay for something in the cloud" [0:08:20].

The sizing decision is the whole game, and it is driven by VRAM rather than by brand. Pick the model first, then rent the smallest card that fits it: "if you want to run something like llama 3.1 70b which is a really classic local llm you typically would only need something like an A40... 39 cents in the secure Cloud to run this with 48 gbt of vram" [0:09:28]. That is the pattern to internalize - a 70B-class open model is a sub-dollar-per-hour rental on a mid-tier card, not a datacenter project. It also means every technique that shrinks the weights directly shrinks the bill, because a quantized model drops into a cheaper VRAM tier.

The flip side is that an hourly rental bills whether or not you are inferencing, so the economics only work when utilization is high enough to beat per-token pricing. That break-even, not the raw hourly rate, is what should decide the architecture.

## Prerequisites

- [Local & Self-Hosted AI](./local-ai.md) - the broader case for running open-weight models yourself.

## Contrasts with

- [Local LLM Hardware Requirements](./local-llm-hardware-requirements.md) - buying, powering, maintaining, and upgrading your own GPU, which is exactly what renting exists to avoid.

## Related

- [Self-Hosting Cost Crossover](./self-hosting-cost-crossover.md) - the utilization math that decides whether a rented GPU beats per-token API pricing.
- [Local vs. Cloud Decision Framework](./local-vs-cloud-decision-framework.md) - where this option sits in the wider choice.
- [Model Quantization](./model-quantization.md) - shrinking the weights drops you into a cheaper VRAM tier.
- [LLM Data Privacy Tiers](./llm-data-privacy-tiers.md) - a rented machine you control is a different privacy posture than a hosted API.
- [LLM Cost Optimization](./llm-cost-optimization.md) - hourly compute versus per-token spend.
- [Local LLMs as Agents](./local-llms-as-agents.md) - the workload most of these rentals actually serve.
- [Agent Deployment](./agent-deployment.md) - shipping the agent that talks to the rented model.

## Tools

- [RunPod](../entities/tools/runpod.md) - Cole's recommended GPU cloud; the A40 pricing above is a RunPod Secure Cloud quote.
- [Ollama](../entities/tools/ollama.md) - the runtime that serves the open model once the machine is up.
- [Docker](../entities/tools/docker.md) - how the model service is packaged onto rented hardware.
- [Llama](../entities/tools/llama.md) - the classic open-weight family being sized here.

## Sources

- [The HARD Truth About Hosting Your Own LLMs](../sources/the-hard-truth-about-hosting-your-own-llms.md) - "[0:08:20] you have to deal with maintenance paying for electricity it's harder to upgrade when the next level of gpus come out... so generally it's a lot more flexible to just pay for something in the cloud"
- [The HARD Truth About Hosting Your Own LLMs](../sources/the-hard-truth-about-hosting-your-own-llms.md) - "[0:09:28] if you want to run something like llama 3.1 70b which is a really classic local llm you typically would only need something like an A40... 39 cents in the secure Cloud to run this with 48 gbt of vram"
