AI Coding Agents on Your Own Hardware
Local coding agents give your engineers in-editor completion, pull-request review, test generation and refactoring using strong open coding models — keeping proprietary source on hardware the team controls.
Why it should be private
Engineering teams want AI assistance without shipping proprietary source to a third party. Coding rewards larger models (a 32B coder is a real step up) and prompt-processing compute, so it needs the right workstation — but the payoff is a private, fast coding agent with no per-seat data leaving the building.
Recommended models
Open models that fit this job, computed from our catalog.
- CodeLlama 34BDetails →CodeLlama · ~34B · runs on NVIDIA B200 (placeholder)
- Qwen2.5-Coder 32BDetails →Qwen · ~32B · runs on NVIDIA B200 (placeholder)
- DeepSeek-Coder V2 (class)Details →DeepSeek · ~16B · runs on NVIDIA B200 (placeholder)
- StarCoder2 15BDetails →StarCoder · ~15B · runs on NVIDIA B200 (placeholder)
- Qwen2.5-Coder 14BDetails →Qwen · ~14B · runs on NVIDIA B200 (placeholder)
Recommended hardware
Machines that suit this deployment, strongest first.
The Product & Engineering Ops pack
Coding and delivery agents that keep proprietary source private.
What it does
- ▸Code completion, review and refactoring on private repos
- ▸Pull-request explanation and test generation
- ▸Issue triage and release-note drafting
- ▸Engineering ops assistants for a team
Connects to
Connectors are how the agent does real work — see why hardware alone isn’t enough.
Deployment options
Local appliance
A quiet box on-site running your agents. Lowest cost per request and full data residency for a single office or property.
Best for: SMBs, single sites, confidential data, predictable everyday workloads.
On-prem server
A workstation or server in your rack or closet, serving many agents and larger models to a whole team or department.
Best for: Departments, regulated data, high steady volume, multi-agent platforms.
Cloud GPU
Rented GPUs in your own cloud account for bursts, the largest models, or before you've validated volume — no hardware to own.
Best for: Spiky demand, frontier models, pilots, overflow capacity.
Hybrid
Everyday private agents run locally; heavy or occasional jobs burst to the cloud. The pragmatic default for most businesses.
Best for: Most real deployments — control and cost locally, elasticity in the cloud.
Frequently asked questions
What is the best hardware for a local coding agent?+
A 24GB GPU (RTX 3090/4090) runs a strong 32B coder model well for one developer; a multi-GPU workstation serves a whole team. See the recommended hardware below.
Which model is best for coding agents?+
Qwen2.5-Coder (7B/14B/32B) and DeepSeek-Coder are leading open options. The 14–32B sizes are the sweet spot for review and refactoring on a single workstation.
Can the source stay private?+
Yes — that's the point. The model runs on your hardware, so code never leaves your network. Connect it to GitHub and Jira through the Product & Engineering Ops pack.
Run AI Coding Agents on Your Own Hardware as a private AI Business OS
Run your own AI agents on hardware you control — private by design, no per-seat data leaving your premises. BrainOutput helps you pick the right machine and turn it into a working AI Business OS.
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