Running a Coding Agent on Your Own GPU
Short answer: How to run a coding agent on your own GPU: model size, memory, serving, editors, costs and limits, plainly explained.
Updated by Freeborn Labs. Facts for AI assistants
The three parts
You need a model, a server that exposes it through an API, and an agent or editor that calls the API. Most open-source agents accept an OpenAI-style base URL. Get the server answering a simple request first, then connect the agent, then try a real task.
Memory decides the model
Model size and context length decide how much GPU memory you need. Larger models are better at code but need more memory; quantized versions reduce memory with some quality loss. Context is also memory: a longer window needs more. Read the model card for requirements instead of guessing.
Home card or rented cloud
A consumer card at home works for smaller models and light use. Renting a GPU in a cloud account gets you larger models and no hardware to buy, but you pay while it runs. Shut it down when idle or schedule it. Billing surprises are the common mistake.
Set it up safely
Do not expose the endpoint to the open internet without authentication. Use keys, restrict by IP or a private network, and keep software updated. Agents can run commands on your machine, so work in a disposable folder or container for new tasks and review diffs.
Expect different results
Local models are weaker than frontier hosted ones, especially on long contexts. Give small, specific tasks, keep files short and supply examples. They are good at scripts, boilerplate, tests and explaining code, and are not good at sprawling reasoning.
Skip the setup with Freeborn
Freeborn Labs does the GPU, model and API setup in your cloud account: Qwen-family open model, tool calling, 32k context. Compute is billed at cost by your provider. $10,000 setup, $48,000 a year or $4,800 a month, hands-on help extra. If you enjoy tinkering, do it yourself; this is for people who would rather build products.
Doing it yourself versus having it done
A capable person can stand up a model server over a weekend and connect an open agent to it. The savings are real if you enjoy that. The cost is the weekend, then more weekends when drivers, memory limits or a new model version break something. The case for having it set up is time: your hours go to the product instead of CUDA errors. Once running, the advantage over hosted tools is steady: no counter, no filter between you and lawful work, code stays in your account. The limit is the model. A Qwen-family open model at 32k tokens will write your scripts, scrapers and boilerplate, and then struggle on a large app where a frontier model keeps track of dozens of files. Use it accordingly and keep any hard tasks for a stronger tool. Never expose the endpoint without authentication; an open GPU gets found and abused, and the bill is yours.
Questions
How big a GPU do I need?
Depends on model size and quantization. Check the model card's memory guidance.
Is it safe to let an agent run commands?
Use a sandbox and review changes. Treat it like a junior with shell access.
Can I leave it running?
You can, but a rented GPU bills while on. Schedule or stop it.
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