dgx-spark-playbooks/nvidia/cli-coding-agent

CLI Coding Agent

Build local CLI coding agents with Ollama

Table of Contents

• Overview
• Claude Code
• OpenCode
• Codex CLI
• Troubleshooting

---

Overview

Basic idea

Use Ollama on DGX Spark to run local coding models and connect a CLI coding agent. This playbook supports three options: Claude Code, OpenCode, and Codex CLI. Each agent talks to Ollama for local inference, so you can work without external cloud APIs.

Choose your CLI agent

Pick the tab that matches the CLI agent you want to use:

• Claude Code: Fastest path to a working CLI agent with a local Ollama model.
• OpenCode: Open-source CLI with provider configuration; this guide targets Ollama.
• Codex CLI: OpenAI Codex CLI configured to run against Ollama locally.

What you'll accomplish

You will run a local coding model on your DGX Spark with Ollama, connect it to your chosen CLI agent, and complete a small coding task end-to-end.

What to know before starting

• Comfort with Linux command line basics
• Experience running terminal-based tools and editors
• Familiarity with Python for the short coding task

Prerequisites

• DGX Spark access with NVIDIA DGX OS 7.3.1 (Ubuntu 24.04.3 LTS base)
• Internet access to download model weights
• Ollama 0.14.3 or newer
• GPU memory depends on the model you choose. Example requirements for GLM-4.7-Flash:
  • 19GB+ for glm-4.7-flash:latest
  • 32GB+ for glm-4.7-flash:q8_0
  • 60GB+ for glm-4.7-flash:bf16

Time & risk

• Duration: ~20-30 minutes (includes model download time)
• Risk level: Low
  • Large model downloads can fail if network connectivity is unstable
  • Older Ollama versions will not load the model
• Rollback: Stop Ollama and delete the downloaded model from ~/.ollama/models
• Last Updated: 01/21/2026
  • First publication

Claude Code

Step 1. Confirm your environment

Description: Verify the OS version and GPU are visible before installing anything.

cat /etc/os-release | head -n 2
nvidia-smi

Expected output should show Ubuntu 24.04.3 LTS (DGX OS 7.3.1 base) and a detected GPU.

Step 2. Install or update Ollama

Description: Install Ollama or ensure it is recent enough for modern coding models.

curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.14.3 sh
ollama --version

If the ollama is already present and the version is 0.14.3 or newer, simply run:

ollama --version

Expected output should show ollama --version as 0.14.3 or newer.

Step 3. Pull GLM-4.7-Flash

Description: Download the model weights to your Spark node.

ollama pull glm-4.7-flash

Optional variants if you need different memory footprints:

ollama pull glm-4.7-flash:q4_K_M
ollama pull glm-4.7-flash:q8_0
ollama pull glm-4.7-flash:bf16

Expected output should show glm-4.7-flash (and any optional variants you pulled) in ollama list.

Step 4. Test local inference

Description: Run a quick prompt to confirm the model loads.

ollama run glm-4.7-flash

Try a prompt like:

Write a short README checklist for a Python project.

Expected output should show the model responding in the terminal.

Step 5. Install Claude Code

Description: Install the CLI tool that will drive the local model.

curl -fsSL https://claude.ai/install.sh | sh

Step 6. Increase context length (optional)

Description: Ollama defaults to a 4096 token context length. For coding agents and larger codebases, set it to 64K tokens. This increases memory usage. For more details on configuring context length, see the Ollama documentation.

Set the context length per session in the Ollama REPL:

ollama run glm-4.7-flash

Then, in the Ollama prompt:

/set parameter num_ctx 64000

Optional method (set globally when serving Ollama):

sudo systemctl stop ollama
OLLAMA_CONTEXT_LENGTH=64000 ollama serve 

Keep this terminal open and run the next step in a new terminal.

Step 7. Connect Claude Code to Ollama

Description: Point Claude Code to the local Ollama server and launch it.

export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_BASE_URL=http://localhost:11434

claude --model glm-4.7-flash

Expected output should show Claude Code starting and using the local model.

Step 8. Complete a small coding task

Description: Create a tiny repo and let Claude Code implement a function and tests.

mkdir -p ~/cli-agent-demo
cd ~/cli-agent-demo

printf 'def add(a, b):\n    """Return the sum of a and b."""\n    pass\n' > math_utils.py
printf 'import math_utils\n\n\ndef test_add():\n    assert math_utils.add(1, 2) == 3\n' > test_math_utils.py

If you do not already have pytest installed:

python -m pip install -U pytest

In Claude Code:

Please implement add() in math_utils.py and make sure the test passes.

Run the test:

python -m pytest -q

Expected output should show the test passing.

Step 9. Cleanup and rollback

Description: Remove the model and stop services if you no longer need them.

To stop the service:

sudo systemctl stop ollama

Warning

This will delete the downloaded model files.

ollama rm glm-4.7-flash

Step 10. Next steps

• Try larger code tasks with the 198K context window
• Experiment with glm-4.7-flash:q8_0 or glm-4.7-flash:bf16 for higher quality
• Use Claude Code on multi-file refactors or test-generation tasks

OpenCode

Step 1. Confirm your environment

Description: Verify the OS version and GPU are visible before installing anything.

cat /etc/os-release | head -n 2
nvidia-smi

Expected output should show Ubuntu 24.04.3 LTS (DGX OS 7.3.1 base) and a detected GPU.

Step 2. Install or update Ollama

Description: Install Ollama or ensure it is recent enough for modern coding models.

curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.14.3 sh
ollama --version

If Ollama is already installed and the version is 0.14.3 or newer, simply run:

ollama --version

Expected output should show ollama --version as 0.14.3 or newer.

Step 3. Pull a coding model

Description: Download a local coding model to your Spark node.

ollama pull glm-4.7-flash

Optional variants if you need different memory footprints:

ollama pull glm-4.7-flash:q4_K_M
ollama pull glm-4.7-flash:q8_0
ollama pull glm-4.7-flash:bf16

Expected output should show your model in ollama list.

Step 4. Install OpenCode

Description: Install the OpenCode CLI using the official Linux instructions.

Follow the install guide at https://opencode.ai/docs, then verify:

opencode --version

Step 5. Configure OpenCode to use Ollama

Description: Point OpenCode to your local Ollama server with an opencode.json.

Create opencode.json in your project directory (or the location you prefer for OpenCode config):

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Ollama (local)",
      "options": {
        "baseURL": "http://localhost:11434/v1"
      },
      "models": {
        "glm-4.7-flash": {
          "name": "glm-4.7-flash"
        }
      }
    }
  }
}

Replace glm-4.7-flash with the model you pulled. If Ollama is running on another host, update the baseURL accordingly.

Step 6. Increase context length (optional)

Description: Ollama defaults to a 4096 token context length. For coding agents and larger codebases, set it to 64K tokens. This increases memory usage. For more details, see the Ollama documentation.

Set the context length per session in the Ollama REPL:

ollama run glm-4.7-flash

Then, in the Ollama prompt:

/set parameter num_ctx 64000

Optional method (set globally when serving Ollama):

sudo systemctl stop ollama
OLLAMA_CONTEXT_LENGTH=64000 ollama serve 

Keep this terminal open and run the next step in a new terminal.

Step 7. Launch OpenCode

Description: Start the OpenCode CLI and select the Ollama provider and model.

opencode

If prompted, select the Ollama provider and the model you configured.

Step 8. Complete a small coding task

Description: Create a tiny repo and let OpenCode implement a function and tests.

mkdir -p ~/cli-agent-demo
cd ~/cli-agent-demo

printf 'def add(a, b):\n    """Return the sum of a and b."""\n    pass\n' > math_utils.py
printf 'import math_utils\n\n\ndef test_add():\n    assert math_utils.add(1, 2) == 3\n' > test_math_utils.py

If you do not already have pytest installed:

python -m pip install -U pytest

In OpenCode:

Please implement add() in math_utils.py and make sure the test passes.

Run the test:

python -m pytest -q

Expected output should show the test passing.

Step 9. Cleanup and rollback

Description: Remove the model and stop services if you no longer need them.

To stop the service:

sudo systemctl stop ollama

Warning

This will delete the downloaded model files.

ollama rm glm-4.7-flash

Step 10. Next steps

• Try other coding models available in Ollama
• Experiment with higher context lengths for larger refactors
• Use OpenCode on multi-file changes or test-generation tasks

Codex CLI

Step 1. Confirm your environment

Description: Verify the OS version and GPU are visible before installing anything.

cat /etc/os-release | head -n 2
nvidia-smi

Expected output should show Ubuntu 24.04.3 LTS (DGX OS 7.3.1 base) and a detected GPU.

Step 2. Install or update Ollama

Description: Install Ollama or ensure it is recent enough for modern coding models.

curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.14.3 sh
ollama --version

If Ollama is already installed and the version is 0.14.3 or newer, simply run:

ollama --version

Expected output should show ollama --version as 0.14.3 or newer.

Step 3. Install Codex CLI

Description: Install the Codex CLI.

npm install -g @openai/codex
codex --version

Step 4. Start Codex with Ollama

Description: Launch Codex with the OSS flag to use Ollama.

codex --oss

By default, Codex uses the local gpt-oss:20b model.

Step 5. Optional settings

Description: Adjust the model or context length if needed.

To use GLM-4.7-Flash with Codex, pull the model and start Codex with -m:

ollama pull glm-4.7-flash
codex --oss -m glm-4.7-flash

To switch to other models, use the -m flag:

codex --oss -m gpt-oss:120b

To use a cloud model:

codex --oss -m gpt-oss:120b-cloud

Codex works best with a large context window. We recommend 64K tokens. For more details, see the Ollama documentation.

Set the context length per session in the Ollama REPL:

ollama run glm-4.7-flash

Then, in the Ollama prompt:

/set parameter num_ctx 64000

Optional method (set globally when serving Ollama):

sudo systemctl stop ollama
OLLAMA_CONTEXT_LENGTH=64000 ollama serve 

Replace glm-4.7-flash with the model you are using (for example, gpt-oss:20b).

Keep this terminal open and run the next step in a new terminal.

Step 6. Advanced configuration (optional)

Description: Set defaults or point Codex at a remote Ollama server.

Create or edit ~/.codex/config.toml:

model = "glm-4.7-flash"
model_provider = "ollama"

[model_providers.ollama]
base_url = "http://localhost:11434/v1"

If Ollama is running on another host, update the base_url accordingly. You can set model to any Ollama model you want Codex to use.

Step 7. Complete a small coding task

Description: Create a tiny repo and let Codex implement a function and tests.

mkdir -p ~/cli-agent-demo
cd ~/cli-agent-demo

printf 'def add(a, b):\n    """Return the sum of a and b."""\n    pass\n' > math_utils.py
printf 'import math_utils\n\n\ndef test_add():\n    assert math_utils.add(1, 2) == 3\n' > test_math_utils.py

If you do not already have pytest installed:

python -m pip install -U pytest

In Codex:

Please implement add() in math_utils.py and make sure the test passes.

Run the test:

python -m pytest -q

Expected output should show the test passing.

Step 8. Cleanup and rollback

Description: Remove the model and stop services if you no longer need them.

To stop the service:

sudo systemctl stop ollama

Warning

This will delete the downloaded model files.

ollama rm gpt-oss:20b

Replace gpt-oss:20b with the model you used.

Step 9. Next steps

• Try other Ollama coding models with Codex CLI
• Experiment with higher context lengths for larger refactors
• Use Codex CLI on multi-file changes or test-generation tasks

Troubleshooting

Symptom	Cause	Fix
ollama: command not found	Ollama not installed or PATH not updated	Rerun `curl -fsSL https://ollama.com/install.sh
Model load fails with version error	Ollama is older than 0.14.3	Update Ollama to 0.14.3 or newer
model not found in Claude Code	Model was not pulled	Run ollama pull glm-4.7-flash and retry
opencode: command not found	OpenCode not installed or PATH not updated	Install OpenCode and open a new shell
OpenCode cannot reach Ollama	baseURL misconfigured or Ollama not running	Set baseURL to http://localhost:11434/v1 and start Ollama
codex: command not found	Codex CLI not installed or PATH not updated	Install Codex CLI and open a new shell
Codex CLI uses the wrong model/provider	~/.codex/config.toml not pointing to Ollama	Set model_provider = "ollama" and base_url = "http://localhost:11434/v1"
connection refused to localhost:11434	Ollama service not running	Start with ollama serve or systemctl start ollama
Slow responses or OOM errors	Model variant too large for GPU memory	Use glm-4.7-flash:q4_K_M or close other GPU workloads

Note

DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. If you see memory pressure, flush the buffer cache with:

sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'