ChatGPT, 
Claude, 
Gemini, 
Grok — all four for If your server suddenly feels slow, or you're seeing your RAM or CPU usage spike after setting up an AI tool, you're not imagining it. AI tools, especially ones that run a model directly on your own server, can be genuinely demanding on hardware. This article explains why that happens and what you can do about it.
Why AI Tools Use So Much RAM and CPU
An AI model, the "brain" behind any AI tool, isn't like a typical app such as a website or an email client. To answer a question or complete a task, it has to load its entire model into memory and run a huge number of calculations for every single word it produces. The bigger and more capable the model, the more RAM and CPU that process takes.
When you're using a cloud AI provider, like the default setup behind an app such as Claude Code, that heavy lifting happens on the AI company's servers, not yours. But when you run a model locally, for example, using Ollama with DeepSeek, all of that work happens on your own server instead. That's the tradeoff for keeping everything private: your server takes on the workload that a cloud provider would otherwise be handling for you.
Common Causes of High Usage
| Cause | Why It Happens |
|---|---|
| Running a local AI model | The model has to be loaded into RAM and processed by the CPU (or GPU, if available) every time it responds. Larger models need significantly more resources than smaller ones. |
| Long conversations or large documents | The more text an AI model has to keep track of at once, the more memory it needs to process it all together. |
| Multiple agents or apps running at once | Each app or agent you run adds to the total load on your server. Two or three running together use noticeably more than just one. |
| Agents running continuously in the background | Tools like Hermes Agent or n8n, set up to run scheduled or recurring tasks, keep using some resources even when you're not actively interacting with them. |
| A multi-step or multi-agent task | Agent frameworks like Paperclip or BMAD, which coordinate several AI agents on one project, can multiply resource use since each agent may be thinking or working at the same time. |
What You Can Do About It
If your server is struggling to keep up, a few options can help:
- Use a smaller model. AI models come in different sizes, and a smaller model uses meaningfully less RAM and CPU, though it may be somewhat less capable in return.
- Avoid running too many apps together. If you've manually installed multiple AI apps on the same server, consider whether all of them truly need to run at the same time.
- Consider a cloud AI provider instead of a local model. If your server can't comfortably handle a local model, connecting your agent to a cloud AI provider shifts that workload off of your server entirely, at the cost of the privacy benefits of running everything locally.
- Upgrade your plan. If you're consistently running out of room, a larger Self-Managed VPS plan, or a VDS (Virtual Dedicated Server) plan for non-shared resources, gives your AI tools more room to run comfortably.
Summary
AI tools use a lot of RAM and CPU because the AI model itself has to be loaded and run for every response, and that workload lands entirely on your server when you're hosting a model locally. Long conversations, multiple apps, and background agents all add to that load. If your server is struggling, using a smaller model, running fewer apps at once, switching to a cloud AI provider, or upgrading your plan are all reasonable ways to lighten the load.