workflowstacks
local-ai

ggrun: Easy AI Model Launching

Get a self-hosted OpenAI-compatible server with ggrun.
264 stars15 forksGoGuide quality 8/10Updated 8/17/2026100% free · open source
What it does

Ggrun launches a self-hosted OpenAI-compatible server with auto-tuned settings for local AI solutions, especially suited for multi-GPU environments.

When to use it
  • •You need to deploy AI models on your own hardware for privacy or security reasons.
  • •Your application requires low-latency AI responses that cloud services cannot guarantee.
  • •You want to leverage the power of multiple GPUs for demanding AI workloads without relying on cloud providers.
Ready-to-paste prompt
go run main.go -model /path/to/your/llama.model -gpu 0,1 to start the server with a specific model and utilize two GPUs.
Heads up: Ensure you have the necessary Go environment set up and that your system meets the hardware requirements for the models you intend to run, especially considering the need for compatible GPUs.
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/ggrun && curl -fsSL https://workflowstacks.com/api/skills/ggrun/claude-skill -o ~/.claude/skills/ggrun/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How ggrun: Easy AI Model Launching works
Codeflow
Free to inspect

Ggrun: Easy AI Model Launching is a very large Go project (~85k lines across 226 code files, plus 15 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.

Size
Very large codebase
~85k lines · 226 code files · days to read — use, don't read
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Docker
No API keys detected
Go 89%Shell 5%Python 4%PowerShell 1%C 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    go/pkg/advisor/artifact_test.go
    Inside go/pkg/ — the main logic begins here
  4. 4
    examples/systemd/ggrun.service
    A worked example — copy this to get going
What's in each folder
go/pkg/Core code — the actual logic172 files
go/cmd/Command-line entry points41 files
docs/Documentation22 files
examples/Examples you can copy1 files
scripts/Helper scripts11 files
tools/Helper scripts6 files
docker/Deployment / infrastructure5 files
native/Folder5 files
READMEHas testsDocumentedCI checksDocker readyExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
raketenkater
Language
Go
Category
local-ai
Published
3/11/2026

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