How to run a Poolside model locally - Poolside

Local Poolside model setup on NVIDIA DGX Spark

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.

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.

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.

Hardware

Laguna S

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.

Laguna XS.2

Laguna XS.2 can run locally on the following hardware:

On NVIDIA systems, Blackwell, Hopper, and Ada GPUs are recommended. Turing and older GPUs may work, but they are not recommended.

Use hardware that meets these minimums:

• 32 GB system RAM on Apple Silicon or unified-memory systems.
• 8 GB VRAM on discrete NVIDIA GPUs.
• Enough free disk space for the selected model tag plus 8 GB on the Ollama models volume.

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.

Model weights and KV cache share memory while the model runs. Use a context length that fits the memory available on your machine.

Step 1: Install Ollama

Install Ollama:

curl -fsSL https://ollama.com/install.sh | sh

For other installation options, see the Ollama documentation.

Confirm Ollama is available:

ollama list

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:

ollama pull laguna-s-2.1:latest

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.

The default Ollama tag is laguna-xs.2:latest, which uses the same model blob as laguna-xs.2:q4_K_M. Use a different tag when you want higher precision and have enough memory.

Pull the tag you want to run. For example, to pull the default tag, run:

ollama pull laguna-xs.2:latest

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:

OLLAMA_LOAD_TIMEOUT=20m ollama run laguna-s-2.1:latest

ollama run laguna-xs.2:latest

Step 4: Install Poolside Agent CLI

Install Poolside Agent CLI so pool can connect to the model that Ollama serves locally:

curl -fsSL https://downloads.poolside.ai/pool/install.sh | sh

Verify the installation:

pool --version

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.

cd <project-path>
ollama launch pool --model laguna-s-2.1:latest

This example uses laguna-xs.2:latest:

cd <project-path>
ollama launch pool --model laguna-xs.2:latest

When the session starts, send a prompt:

Summarize this project and suggest one small improvement.

The setup works when pool starts an agent session and responds to the prompt.

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:

• Use Poolside in Zed
• Use Poolside in JetBrains
• Use Poolside in another ACP-compatible editor

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.