Zinc: Local LLM Inference
Zinc allows founders to run large language models locally on their own hardware, specifically AMD GPUs and Apple Silicon, for private and efficient AI inference.
- •You need to keep your AI data and models private and offline.
- •Your application requires low-latency and high-throughput AI inference.
- •You want to develop and test AI models on your local machine before deploying them to a cloud or production environment.
Run the command `zig-out/bin/zinc -c config.toml -p 'What is the meaning of life?'` to test your local language model with a sample prompt.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Expect 20–40 minutes in a terminal — or let your AI agent drive it.
mkdir -p ~/.claude/skills/zinc && curl -fsSL https://workflowstacks.com/api/skills/zinc/claude-skill -o ~/.claude/skills/zinc/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Zinc: Local LLM Inference is a very large Zig project (~121k lines across 198 code files, plus 6 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, MIT license, has a test suite.
- 1README.mdStart here — what it does and how to install it
- 2AGENTS.mdThe instructions the AI actually follows
- 3package.jsonDependencies and the commands it exposes
- 4src/main.zigInside src/ — the main logic begins here
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Expect 20–40 minutes in a terminal — or let your AI agent drive it.
mkdir -p ~/.claude/skills/zinc && curl -fsSL https://workflowstacks.com/api/skills/zinc/claude-skill -o ~/.claude/skills/zinc/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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