rigspark

# <img src="crates/rigspark-gui/static/mascot-avatar.jpg" alt="" width="44" height="44"> RigSpark: Check Which Local LLMs Your Computer Can Run [](https://github.com/shashankswe2020-ux/rigspark/actions/workflows/ci.yml) [](https://github.com/shashankswe2020-ux/rigspark/releases/latest) [](https://crates.io/crates/rigspark-cli) [](https://crates.io/crates/rigspark-cli) [](https://docs.rs/rigspark-core) [](rust-toolchain.toml) [](https://github.com/shashankswe2020-ux/rigspark/releases/latest) [](https://shashankswe2020-ux.github.io/rigspark/) [](LICENSE) **Which local LLMs can your computer run? Find out before downloading the weights.** RigSpark scores your hardware and gives every model a `yes / slow / no` verdict, a memory-fit explanation, and an estimated tok/s range. Then it verifies, serves, and chats with the model you pick. It is an open-source, privacy-first Rust CLI with a terminal UI and a browser workspace for macOS, Linux, and Windows. [](assets/rigspark.mp4) *20-second tour:* `rigspark recommend` ranks the offline catalog on an arm64 Mac with 34 GiB of usable RAM and lists the models that won't fit; `rigspark gui` then shows the same verdicts and a local chat. [Watch in 1080p with sound](assets/rigspark.mp4) · [Earlier demo on YouTube](https://youtu.be/MI2wfI1eeCM?si=QA2teeDmeT_fNIqf) ## Why RigSpark - **Know before you download.** Estimates RAM and GPU/VRAM fit for models such as Llama, Qwen, Mistral, and Gemma. - **Honest numbers.** Advice uses a bundled offline catalog. Unknown figures stay `unknown`; estimates are not benchmarks. - **Safe by default.** Managed downloads are integrity-checked, and servers bind to `127.0.0.1`. - **Bring your backend.** Works with **Ollama**, **llama.cpp**, **MLX** (Apple Silicon), and **LM Studio** (attach-only). ## Install Homebrew (macOS and Linux): ```bash brew install shashankswe2020-ux/tap/rigspark ``` Windows and other platforms: download a [prebuilt archive](https://github.com/shashankswe2020-ux/rigspark/releases/latest), extract it, and add the folder to `PATH`. Keep `rigspark`, `llmup`, and `rigspark-gui` together. The binaries need no Node.js, Python, or compiler; inference backends have their own requirements. More: [Cargo, checksums, unsigned macOS archives, and upgrades](docs/references/guide.md#install) · [Docker caveats](docs/references/guide.md#docker) ## Quick Start ```bash rigspark recommend # rank models for your hardware rigspark can-run llama3.1:8b # check one model before downloading rigspark catalog --all # browse the offline catalog rigspark up llama3.1:8b # pull, verify, and serve rigspark chat # chat with the active model rigspark down # stop when done ``` Advice works without a backend. To serve and chat, install [Ollama](https://ollama.com) or another [supported backend](docs/references/guide.md#supported-backends). Use `rigspark --help` for all commands, `--json` for scripting, and `--accessible` for screen readers. `llmup` remains a compatibility alias. ## Catalog Updates RigSpark can activate a signed model catalog independently of an application release. Updating is always explicit; recommendations and normal startup remain offline. [Watch the catalog update flow](assets/catalog-update.mp4) ```bash rigspark catalog --status # show the active source, revision, digest, and model count offline rigspark catalog --update # download, verify, and atomically activate the official catalog ``` The Models view exposes the same provenance and an **Update catalog** action. Invalid signatures, incompatible catalogs, and network failures leave the current snapshot unchanged. RigSpark can recover through the previous verified snapshot and then its bundled catalog, with visible warnings. `rigspark catalog --refresh` is different: it previews maintainer enrichment from the bundled registry snapshot and does not install a published catalog. New model admission still requires reviewed evidence; a newer catalog date is not a promise of complete upstream coverage. See [catalog behavior, trust, and launch availability](docs/references/guide.md#independent-catalog-updates). ## Browser Workspace Run **`rigspark gui`** to pick a model that fits and chat with it, with agents, skills, and MCP tools. Local chat stays on your machine; cloud harnesses and external tools can send data to their providers.  ## References - [Commands, installed Ollama models, and custom context](docs/references/guide.md#commands) - [Terminal UI](docs/references/guide.md#terminal-ui) · [Browser workspace, agents, and tools](docs/references/guide.md#browser-gui) - [Catalog and maintenance](docs/references/guide.md#model-catalog) · [How advice works](docs/references/guide.md#how-advice-works) - [Scripting and exit codes](docs/references/guide.md#scripting--exit-codes) · [Performance measurements](docs/references/guide.md#performance-10-native-vs-0114-node) - [FAQ](docs/references/guide.md#faq) · [Troubleshooting](docs/references/guide.md#troubleshooting) · [Tool comparisons](docs/references/guide.md#rigspark-vs-ollama) - [Development and testing](docs/references/guide.md#development) · [Specification](docs/specs/rigspark.md) · [Changelog](CHANGELOG.md) [MIT License](LICENSE) **Keywords:** local LLM, run LLM locally, LLM hardware requirements, VRAM calculator, tokens per second, Ollama, llama.cpp, MLX, LM Studio, Rust CLI.