ClawHub

gpu monitor

MCP Tools

Provides real-time NVIDIA GPU usage and memory stats, plus Ollama model layer GPU/CPU distribution via server.log parsing with live updates.

Install

openclaw skills install gpu-monitor

GPU Monitor - Ollama Real-time GPU Monitoring Skill

Overview

This skill provides real-time GPU monitoring for local Ollama models. It monitors:

  • GPU name and memory usage with utilization percentage (e.g., 8.5/10.0 GB = 85%)
  • Model layer distribution (GPU vs CPU offloading) via Ollama server.log parsing
  • Live status updates every 2 seconds

⚠️ Framework Dependency: This skill is specifically designed for the Ollama framework (https://ollama.ai).
📝 Log Requirement: Requires access to Ollama's server.log file at a configurable path to parse model layer information.

Features

✅ Ollama-specific monitoring: Automatically parses server.log for model info when available
✅ Layer distribution tracking: Shows GPU layers, total layers, and CPU offload percentage
✅ Memory visualization: Displays memory used/total with real-time utilization %
✅ Cross-platform: Works on Windows/Linux/macOS with NVIDIA GPUs via nvidia-smi
✅ Real-time updates: Configurable refresh interval (default: 2 seconds)
✅ Flexible configuration: Specify Ollama log path via CLI --ollama-log=PATH or config file
✅ Graceful degradation: Shows GPU metrics even without Ollama installed

Installation

# Via ClawHub
clawhub install gpu-monitor-skill

# Or manual clone
git clone <repository-url> ~/.openclaw/skills/gpu-monitor

Usage (Local Testing)

# Basic usage - monitors local GPU
python ~/.openclaw/clawhub/gpu-monitor-skill/gpu_monitor.py --interval=3

# With Ollama log path for layer tracking
python ~/.openclaw/clawhub/gpu-monitor-skill/gpu_monitor.py \
    --ollama-log="C:\Users\zugzwang\AppData\Local\Ollama\server.log" \
    --interval=2

# Using config file (create ~/.openclaw/gpu_monitor_config.json)
{
  "update_interval_seconds": 2,
  "ollama_log_path": "/path/to/server.log",
  "quiet_mode": false
}

Configuration

Create ~/.openclaw/gpu_monitor_config.json:

{
  "update_interval_seconds": 2,
  "ollama_log_path": "/path/to/Ollama/server.log",
  "quiet_mode": false
}
FieldTypeDescription
update_interval_secondsintRefresh interval (default: 2)
ollama_log_pathstringPath to Ollama server.log (optional)
quiet_modeboolDisable banner messages

Output Examples

With Ollama Layer Info

┌─[Update #1] 12:30:45
├─ GPU:         NVIDIA GeForce RTX 3080
├─ Memory Used: 8.5/10.0 GB (85.0%)
├─ Log Time:    [实时模式 - 无层数数据]
├─ GPU Layers:  [实时模式]

With Layer Data

┌─[Update #1] 12:31:02
├─ GPU:         NVIDIA GeForce RTX 3080
├─ Memory Used: 7.2/10.0 GB (72.0%)
├─ Log Time:    time=2026-03-27T12:31:02+08:00
├─ GPU Layers:  32 / 33
├─ CPU Layers:  1 (3.0%)

Without Ollama

┌─[Update #1] 12:32:15
├─ GPU:         NVIDIA GeForce RTX 3080
├─ Memory Used: 9.2/10.0 GB (92.0%)
├─ Log Time:    [实时模式 - 无层数数据]
├─ GPU Layers:  [实时模式]

Prerequisites

  • Python 3.7+
  • NVIDIA GPU with nvidia-smi available (Windows/Linux/macOS)
  • (Optional) Ollama server for layer tracking

License

MIT License

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