RigSpark：硬件感知的 Rust CLI，下载前判断本地 AI 模型能否运行

Know what your computer can run before you download it.

RigSpark is a hardware-aware native Rust CLI that gives local AI models a yes / slow / no verdict, explains the fit, and estimates tokens per second before you commit to the weights.

Website · Install · Guide · Latest release

New in 3.0: a redesigned browser workspace. Every feature is still there, but each control appears only when you need it, and the Models view now loads the full ranked catalog. See what changed →

Let Sparky check your rig

RigSpark keeps the first question simple: will this model actually run here? The answer comes from a curated offline catalog and your detected hardware, not from a download you have already waited for.

1. Install RigSpark

Homebrew on macOS or Linux:

brew install shashankswe2020-ux/tap/rigspark

On Windows and other platforms, download a prebuilt archive, extract it, and add the folder to PATH. Keep rigspark, llmup, and rigspark-gui together.

The RigSpark binaries need no Node.js, Python, or compiler. Inference backends have their own requirements.

If Sparky helps you, please consider sponsoring the project: https://buymeacoffee.com/shashanksw9

Cargo, checksums, unsigned macOS archives, and upgrades → · Docker caveats →

2. Sparky sizes up your hardware

Start with advice. It is deterministic, works offline, and does not require an inference backend:

rigspark recommend
rigspark can-run llama3.1:8b
rigspark catalog --all

• recommend ranks the catalog for this machine.
• can-run checks one model before you download it.
• catalog --all lets you browse the bundled model data.

Use --json for automation and --accessible for screen-reader-friendly output. llmup remains a compatibility alias.

3. Read the face: yes, slow, or no

Every verdict includes the reason. When RigSpark cannot source a figure, it says unknown rather than inventing one. Throughput ranges are estimates, not benchmarks.

4. Pick a model, verify it, and start

Once you have a fit, move from advice to a running model:

rigspark up llama3.1:8b    # pull, verify, and serve
rigspark chat              # chat with the active model
rigspark ls                # inspect managed models
rigspark switch            # change the active model
rigspark down              # stop when done

RigSpark supports:

• Ollama — recommended default and managed child process
• llama.cpp — native local serving
• MLX — Apple Silicon
• LM Studio — attach to an already running server

Managed weights pass an integrity check before serving. Servers bind to 127.0.0.1 by default.

Backend setup and lifecycle details →

5. Continue in the browser workspace

rigspark gui

Choose a model, inspect the same hardware verdicts, chat, and use agents, skills, and MCP tools from the loopback-only browser workspace.

• One model chip in the top bar holds the model, runtime, harness, and agent pickers.
• Models lists the whole ranked catalog with search, Runs well / Slow / Won't fit filters, and a Fit settings popover for context and KV cache.
• The inspector (⌘I, or click Sparky) shows live memory, CPU, disk, and the latest call's token usage. Sparky's mood follows real RAM pressure.
• Light and dark themes, with ⌘1–⌘5 to switch views.

Local chat stays on your machine. Cloud harnesses and external tools can send data to their own providers, so RigSpark keeps those boundaries visible.

Browser workspace, agents, and tools →

6. Let Sparky create images and short videos

RigSpark can orchestrate open-weight image and video models through your own ComfyUI installation.

rigspark catalog --generation
rigspark generate image --prompt "a lighthouse at dusk" --comfyui-dir ~/ComfyUI
rigspark generate video --prompt "a fox running in snow" --comfyui-dir ~/ComfyUI
rigspark generate --tui

Start ComfyUI on its default loopback address, 127.0.0.1:8188. RigSpark downloads pinned weights, verifies their SHA-256 digest, installs only its built-in local workflows, and refuses paid Partner API nodes. Generation speed stays unknown when no defensible measurement is available.

In rigspark gui, switch the composer from Text to Image or Video. Generated media renders inline and is never sent to the language model.

Image and video generation specification →

Why the answers are trustworthy

• Offline advice. Recommendation commands use a bundled, curated catalog and make no network calls.
• Honest unknowns. Missing bandwidth, model geometry, or measurements stay visibly unknown.
• Fail-closed integrity. Managed weights must match their catalog digest, or a documented size-floor fallback, before RigSpark serves them.
• Loopback by default. Local servers listen on 127.0.0.1, not the public network.
• Reproducible decisions. The same hardware and catalog produce the same recommendation.
• One adapter boundary. Ollama, llama.cpp, MLX, and LM Studio sit behind the same runtime lifecycle and safety gates.

Update the catalog when you choose

Catalog updates are explicit; recommendations and normal startup remain offline.

rigspark catalog --status
rigspark catalog --update

RigSpark verifies the signed catalog before activating it atomically. An invalid signature, incompatible schema, or network failure leaves the current snapshot unchanged. Recovery falls back through the previous verified snapshot and then the bundled catalog, with visible warnings.

New models arrive weekly. A scheduled pipeline admits every new Ollama library model whose facts it can read from the shipped artifact (size, digest, context, architecture, open license), plus Comfy-Org image and video releases, and publishes a signed catalog once the quality gate passes. These entries are marked auto-sourced, keep fail-closed digest verification, and show unknown for anything that could not be sourced. Image and video models without a built-in workflow yet get a fit verdict and are labelled workflow coming.

rigspark recommend --month 1        # only models released or added in the last month
rigspark catalog --all --month 3

In the terminal UI, m cycles the same 1, 2 and 3 month windows; the browser workspace has a Released in selector.

rigspark catalog --refresh is a maintainer preview of enrichment against the bundled registry snapshot; it does not install a published catalog.

Catalog provenance, trust, and recovery →

A 20-second tour

The tour runs rigspark recommend, shows models that do and do not fit, then opens the same verdicts and a local chat in rigspark gui.

Watch in 1080p with sound · Earlier demo on YouTube

Find your next command

Run rigspark --help for the complete CLI.

Project map

• crates/rigspark-core/ — catalog, memory sizing, ranking, advice, and reports
• crates/rigspark-runtime/ — hardware detection, backend adapters, lifecycle, state, and harnesses
• crates/rigspark-gui/ — loopback HTTP/SSE host and embedded browser client
• crates/rigspark-cli/ — native CLI, terminal UI, and catalog maintenance
• apps/desktop/src-tauri/ — native desktop shell

Build from source

RigSpark is a Rust workspace pinned to Rust 1.98.1:

cargo build --workspace --locked
cargo test --workspace --locked -- --test-threads=2
cargo clippy --workspace --all-targets --locked -- -D warnings
cargo fmt --all -- --check
cargo native-retirement

No Node.js or TypeScript tooling is part of the project.

Keep exploring

• Commands, custom context, and installed models
• Terminal UI
• How hardware advice works
• Scripting and exit codes
• FAQ and troubleshooting
• Development guide
• Project specification
• Changelog

Let Sparky check your rig.
Download RigSpark · Star on GitHub · Buy me a coffee

Support RigSpark

RigSpark is free, MIT-licensed, and built in spare time. If it saved you a download that would never have run, you can buy me a coffee ☕.

MIT License

Keywords: local LLM, run LLM locally, LLM hardware requirements, VRAM calculator, tokens per second, Ollama, llama.cpp, MLX, LM Studio, Rust CLI.