Ktop – a themed terminal system monitor with charts and OOM tracking
I built a terminal system monitor that fills a gap I kept hitting when running local LLMs: GPU usage and memory (for both NVidia and AMD) alongside CPU usage and memory, temps, upload, download and OOM kill tracking. All in one view with 50 colour themes. Consumes less cpu usage than glances (in my testing). One line install.
Ktop – a themed terminal system monitor with charts and OOM tracking launched on February 10, 2026 with 8 votes, #1,230 of 16,627 launches that month and more than 75% of that year's launches.
What it does
Terminal system resource monitor for hybrid LLM workloads - brontoguana/ktop
A terminal-based system resource monitor built for tracking resource usage when running hybrid LLM workloads. curl -sSfL https://raw.githubusercontent.com/brontoguana/ktop/master/install.sh | bash Downloads the latest binary and installs it to /usr/local/bin (will prompt for sudo if needed). Run the same command again to upgrade. ktop uninstall Build from source git clone https://github.com/brontoguana/ktop.git cd ktop/ktop-rs cargo build --release sudo cp target/release/ktop /usr/local/bin/ Usage # Run with defaults (1s refresh) ktop # Custom refresh rate ktop -r 2 # Start with a specific theme ktop --theme " Tokyo Night " # Simulation mode (fake OOM kills, profiling to…from github.com
In the maker’s words, at launch
Built this because I wanted nvtop + btop in one view while tuning local hybrid LLM inference. Supports themes, charts, and OOM kill tracking. Written in Python.
Does a similar job
all alternatives →- UB/usr/bin/htop2017 · 0xfee1dead.top · ▲219
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Utilyze – an open source GPU monitoring tool more accurate than nvtopApr 2026 · systalyze.com · ▲128The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems…
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