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Use Ollama, Poolside Agent CLI, and an open-weight Poolside model to run a coding agent on your own hardware, such as Laguna S 2.1 on NVIDIA DGX Spark. With this setup, Ollama serves the model locally and pool connects to it from your project directory. Your code and model traffic stay on the machine where you run the local model server.

Local Poolside model setup on NVIDIA DGX Spark

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Choose a model

Not sure which one? Start with Laguna XS.2 if your machine has less than 128 GB of memory. Move to Laguna S 2.1 when you have a high-memory workstation or server.

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How it works

A local Poolside agent uses four pieces:
  • A Laguna model: The open-weight model that fits your hardware.
  • Ollama: The local runtime. Ollama pulls the model and serves it through an OpenAI-compatible endpoint on your machine.
  • Poolside Agent CLI: The terminal-based coding agent. Use the pool command to work with the local model from your project directory.
  • ACP editors: Editors compatible with the Agent Client Protocol (ACP), such as JetBrains and Zed, can use the same local agent through ACP.

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Prerequisites

Before you install the local agent, check that your machine has enough memory, GPU support, and disk space for the model you want to run.

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Hardware

Laguna S 2.1 can run locally on the following hardware:Use hardware that meets these minimums:
  • 128 GB of unified or system memory. The default q4_k_m weights are 75 GB, and the weights and KV cache share the same memory pool.
  • Enough free disk space for the selected model tag plus 8 GB on the Ollama models volume.

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Memory and context

Model weights and KV cache share memory while the model runs. On a 128 GB box the 75 GB of weights leave limited room, so start with a modest context and raise it as memory allows.

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Step 1: Install Ollama

Install Ollama:
For other installation options, see the Ollama documentation. Confirm Ollama is available:

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Step 2: Choose and pull a model tag

The default Ollama tag is laguna-s-2.1:latest, which uses the same model blob as laguna-s-2.1:q4_k_m. Use a higher-precision tag when you have enough memory.Pull the tag you want to run. For example, to pull the default tag, run:
The first load reads 75 GB or more off disk, which can exceed Ollama’s default five-minute model-load timeout. If the first run times out, set OLLAMA_LOAD_TIMEOUT=20m.

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Step 3: Test the model in Ollama

You can chat with the model directly through Ollama to confirm it works. Use the same tag you pulled in the previous step:

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Step 4: Install Poolside Agent CLI

Install Poolside Agent CLI so pool can connect to the model that Ollama serves locally:
Verify the installation:
The command prints the installed version. For installation details, see Install Poolside Agent CLI.

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Step 5: Launch pool against the local model

Go to the project directory you want to work in, then point pool at the model Ollama serves on your machine. Use the same tag you used in step 2.
When the session starts, send a prompt:
The setup works when pool starts an agent session and responds to the prompt.

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Optional: Work in your editor

Because pool supports ACP, you can connect the same local agent to any ACP-compatible editor instead of working from the terminal:

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Next steps

Learn what you can do with pool

Keep working in the terminal with interactive sessions, automation, and other Poolside Agent CLI workflows.

Configure your agent

Shape agent behavior and control what it can do.