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An agent on your own hardware

Run a coding agent locally on your own computer

Keep source code and the model on your own network, connect editor and tools deliberately, and choose a setup fast enough for real project work.

Start with the practical steps Choose suitable hardware

In plain language

A local agent connects a local model to your project tools

The model runs on your computer or a server in your network. An agent client supplies project files and can run approved commands such as tests. Your source does not need to reach a remote model provider, although editor extensions must also be configured for local use.

Good tasks to delegate

When running locally is attractive

When running locally is attractive.

Confidential source

Files and questions stay on devices you control.

Frequent repeated work

After setup, there is no per-request model bill.

Internal networks

The agent can work without open internet access.

How to set it up

Start with one local agent safely

Use a small test project and only the tools needed for the task.

  1. Choose a repeated task

    Install a local model runtime and a coding model that fits available memory.

  2. Give it a goal and project

    Connect an agent client to the local model and open a small project.

  3. Let the agent do the work

    Allow file reading, contained edits and one harmless test command first.

  4. Try the visible result

    Try a known bug and check quality, speed and network connections.

Where you still look yourself

Check the complete local connection

The editor, extension and search index matter as much as the model.

  • The model address points to your own device.
  • Cloud fallback and online search are off when unwanted.
  • Commands and writable folders are appropriately limited.
  • An offline test confirms the core workflow still runs.

Choose suitable hardware

Hardware for a local coding agent

These systems provide different levels of memory and compute for a model, project context and development tools running together.

HP OMEN 45L RTX 5090 / 64 GB RAM

For fast agent runs

GeForce RTX 5090 32 GB · Core Ultra 9 285K · 64 GB RAM · 2 TB SSD

HP OMEN 45L RTX 5090 / 64 GB RAM

A strong choice when local models prepare edits while tests or containers remain active.

GPU memory
32 GB dedicated VRAM
System memory
64 GB DDR5
Storage
2 TB SSD

Watch for: Check graphics memory and sustained noise on the exact configuration.

View configuration*
ASUS Ascent GX10 128 GB unified memory / 4 TB SSD

For large project context

NVIDIA GB10 · 128 GB coherent unified memory · 4 TB SSD · DGX OS

ASUS Ascent GX10 128 GB unified memory / 4 TB SSD

Substantial shared memory helps an agent keep more files and a larger model available together.

Model memory
128 GB coherent unified
Storage
4 TB NVMe SSD
Platform
Arm/Linux with NVIDIA stack

Watch for: Specialized systems generally cost more than a conventional development PC.

View configuration*
HP Z2 Mini G1a 128 GB RAM / 1 TB SSD

For repeated jobs

Ryzen AI Max+ PRO 395 · Radeon 8060S · 128 GB unified memory · 1 TB SSD

HP Z2 Mini G1a 128 GB RAM / 1 TB SSD

Extra capacity suits longer agent runs or several project tasks handled in succession.

Model memory
128 GB shared
Storage
1 TB and a second M.2 slot
Support
Three-year workstation warranty

Watch for: Estimate the real benefit with the model and repository you intend to use.

View configuration*

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Compare all local-AI systems at AI Model PC →

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Official documentation and further reading

Technical references are linked to the original project or manufacturer documentation.