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What you can do with LLM Visuals

A Rust terminal dashboard that reads a locally running LLM server and GPU drivers and shows live throughput, latency, GPU load, context fill and expert routing.

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DingoOz/llm-visuals on GitHub

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What do you want to do?

4 use cases

  • Watch tokens per second and TTFT on a local server

    Attach the dashboard to a running llama-server and see tok/s, time to first token and GPU load update live.

    View prompt
    Using the repo DingoOz/llm-visuals, run `cargo run --release` against a llama-server listening on <server-port> with metrics enabled. Expected output: a dashboard listing that server with live tokens per second and time to first token while a request runs. Verify the model name and port shown match the server you started.

  • Preview every dashboard view with no GPU or model

    Run the built-in demo to see all views on synthetic data before setting up a real server.

    View prompt
    Using the repo DingoOz/llm-visuals, run `cargo run --release -- --demo` in a terminal of at least 140 by 44 characters. Expected output: a dashboard showing a synthetic server and two synthetic GPUs. Verify that pressing `q` restores the terminal cleanly.

  • Record every request to a SQLite log for analysis

    Start the dashboard with a log database so request rows are saved for later review.

    View prompt
    Using the repo DingoOz/llm-visuals, start the dashboard with `cargo run --release -- --log-db llm.db` while a llama-server is running. Expected output: an llm.db file in the repo folder with one row per request. Verify the row count rises after you send a prompt.

  • Build release binaries for six OS and CPU targets

    Cross-compile archives for Linux, macOS and Windows on x86-64 and ARM64 from one machine.

    View prompt
    Using the repo DingoOz/llm-visuals, run `./scripts/cross-build.sh` from the repo root. Expected output: archives for each target this host can link, written to dist/. Verify the count of archives matches the number of targets the script reports as buildable.

How to use LLM Visuals

  1. Check you have what it needs: Rust toolchain 1.75 or newer; A C compiler, needed by the bundled SQLite used for --log-db; A locally listening llama-server for throughput panels (the demo mode needs none).
  2. Open DingoOz/llm-visuals on GitHub and read the README and license.
  3. Install a Rust toolchain (1.75 or newer), then run git clone https://github.com/DingoOz/llm-visuals.
  4. Run cd llm-visuals and then cargo run --release -- --demo to confirm the layout renders with synthetic data.
  5. Start your model with metrics enabled, for example llama-server --metrics, using the flag the README shows as llama-server.exe --metrics on Windows. (inferred, check the README)
  6. Run cargo run --release in the repo folder so the dashboard detects the running server.

These steps come from our top use case, Watch tokens per second and TTFT on a local server, which also has a copy-paste agent prompt.

LLM Visuals alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Rust toolchain 1.75 or newer
  • A C compiler, needed by the bundled SQLite used for --log-db
  • A locally listening llama-server for throughput panels (the demo mode needs none)
  • nvidia-smi for NVIDIA GPU panels, xpu-smi for Intel, the amdgpu driver for AMD, or macmon on Apple silicon
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Questions about LLM Visuals

What is LLM Visuals?

A Rust terminal dashboard that reads a locally running LLM server and GPU drivers and shows live throughput, latency, GPU load, context fill and expert routing. It is an open-source project on GitHub: https://github.com/DingoOz/llm-visuals.

Who is LLM Visuals for?

Developers and local-AI tinkerers who run llama.cpp or similar servers on their own machine and want to see what the model is doing.

What can I do with LLM Visuals?

4 use cases so far, for example: Watch tokens per second and TTFT on a local server; Preview every dashboard view with no GPU or model; Record every request to a SQLite log for analysis. Each has steps and a copy-paste agent prompt.

What do I need to run LLM Visuals?

Rust toolchain 1.75 or newer; A C compiler, needed by the bundled SQLite used for --log-db; A locally listening llama-server for throughput panels (the demo mode needs none); nvidia-smi for NVIDIA GPU panels, xpu-smi for Intel, the amdgpu driver for AMD, or macmon on Apple silicon.

What are alternatives to LLM Visuals?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is LLM Visuals safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.