ktop: Unified GPU/CPU Monitor for Hybrid Workloads

What It Is

ktop is a terminal-based monitoring tool that displays GPU and CPU metrics in a single interface. Unlike traditional system monitors that focus on either CPU or GPU resources, ktop consolidates both into one view, eliminating the need to switch between separate monitoring applications.

The tool emerged from a practical frustration: developers running hybrid workloads were constantly juggling multiple terminal windows, each showing different resource types. ktop solves this by presenting GPU utilization, memory usage, temperature, and CPU statistics side-by-side in real-time. The interface supports customizable themes for different terminal color schemes and preferences.

Built for terminal environments, ktop operates without requiring a graphical interface, making it suitable for remote servers, SSH sessions, and headless systems where developers need visibility into resource consumption across both processing units.

Why It Matters

Hybrid workloads are becoming standard practice in machine learning infrastructure. Modern LLM deployments increasingly split tasks between GPUs and CPUs based on computational characteristics - GPUs excel at parallel prompt processing while CPUs can handle sequential token generation more cost-effectively. This architectural pattern requires monitoring both resource types simultaneously to identify bottlenecks and optimize performance.

Traditional monitoring approaches force developers to run nvtop for GPU metrics and btop (or htop) for CPU stats in separate terminals. This fragmentation creates cognitive overhead and makes it harder to correlate resource usage patterns. When a model runs slowly, is the GPU saturated? Is the CPU becoming a bottleneck during token generation? Answering these questions requires mental context-switching between different tools.

Data scientists and ML engineers benefit most from unified monitoring. Training pipelines that preprocess data on CPUs before GPU training, inference servers handling mixed workloads, and development environments running multiple models simultaneously all generate resource patterns that span both processing types. Having consolidated visibility helps teams make informed decisions about resource allocation, instance sizing, and workload distribution.

The cost optimization angle matters too. Cloud GPU instances are expensive, and hybrid architectures that offload appropriate tasks to CPUs can reduce infrastructure costs significantly. However, optimizing these systems requires understanding how both resources are utilized - something that’s difficult when metrics are scattered across different tools.

Getting Started

The project is available on GitHub at https://github.com/brontoguana/ktop. Installation requires cloning the repository and following the build instructions in the README:

# follow install instructions in README

After installation, launching ktop provides an immediate view of system resources. The interface updates in real-time, showing GPU memory allocation, utilization percentages, temperatures, and CPU metrics including per-core usage and system load.

For developers working with hybrid LLM runtimes, ktop becomes particularly valuable during debugging sessions. When prompt processing times spike, the unified view immediately shows whether GPU memory is exhausted or if CPU cores are maxed out during token generation. This visibility accelerates troubleshooting compared to switching between monitoring tools.

Theme customization allows adaptation to different terminal color schemes and personal preferences, ensuring readability across various development environments.

Context

ktop joins a crowded field of system monitoring tools, each with different strengths. nvtop provides detailed GPU metrics but ignores CPU resources. btop and htop offer comprehensive CPU monitoring with beautiful interfaces but lack GPU visibility. Tools like glances attempt broader system monitoring but often provide less detailed GPU metrics than specialized tools.

The tradeoff with ktop is specialization - it focuses specifically on GPU/CPU monitoring rather than attempting to cover every system metric. Network statistics, disk I/O, and other system resources may require separate tools. This focused approach makes sense for ML workloads where GPU and CPU resources dominate performance characteristics.

Alternatives include running multiple monitoring tools in tmux or screen sessions, which provides flexibility but requires manual layout management. Cloud-based monitoring solutions like Prometheus with Grafana offer more sophisticated visualization and historical data but require additional infrastructure and don’t provide the immediate, lightweight terminal experience that ktop delivers.

For teams running containerized workloads, ktop works within container contexts where developers need visibility into resource consumption without installing heavy monitoring stacks. The terminal-based approach integrates naturally into existing development workflows without requiring browser access or additional services.