{"data":{"slug":"containers-ramalama","name":"ramalama","tagline":"RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of con","github_url":"https://github.com/containers/ramalama","owner":"containers","repo":"ramalama","owner_avatar_url":"https://avatars.githubusercontent.com/u/5874934?v=4","primary_language":"Python","stars":2957,"forks":348,"topics":["ai","containers","cuda","hacktoberfest","hip","inference-server","intel","llamacpp","llm","podman","vllm"],"archived":false,"github_pushed_at":"2026-07-14T20:45:20+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/containers-ramalama","markdown_url":"https://www.graphcanon.com/tools/containers-ramalama.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/containers-ramalama","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=containers-ramalama","description":"RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers.","homepage_url":"https://ramalama.ai","license":"MIT","open_issues":103,"watchers":34,"ai_summary":null,"readme_excerpt":"### Install on macOS (Self-Contained Installer)\nDownload the self-contained macOS installer that includes Python and all dependencies:\n\n1. Download the latest `.pkg` installer from [Releases](https://github.com/containers/ramalama/releases)\n2. Double-click to install, or run: `sudo installer -pkg RamaLama-*-macOS-Installer.pkg -target /`\n\nSee [macOS Installation Guide](docs/MACOS_INSTALL.md) for detailed instructions.\n\n---\n\n### Install on Fedora\nRamaLama is available in [Fedora](https://fedoraproject.org/) and later. To install it, run:\n```\nsudo dnf install ramalama\n```\n\n---\n\n### Install via PyPI\nRamaLama is available via PyPI at [https://pypi.org/project/ramalama](https://pypi.org/project/ramalama)\n```\npip install ramalama\n```\n\n---\n\n### Install script (Linux and macOS)\nInstall RamaLama by running:\n```\ncurl -fsSL https://ramalama.ai/install.sh | bash\n```\n\n---\n\n### Install on Windows\nRamaLama supports Windows with Docker Desktop or Podman Desktop:\n```powershell\npip install ramalama\n```\n\n**Requirements:**\n- Python 3.9 or later\n- Docker Desktop or Podman Desktop with WSL2 backend\n- For GPU support, see [NVIDIA GPU Setup for WSL2](docs/readme/wsl2-docker-cuda.md)\n\n**Note:** Windows support requires running containers via Docker/Podman. The model store uses hardlinks (no admin required) or falls back to file copies if hardlinks are unavailable.\n\n---\n\n## Hardware Support\n\n| Hardware                           | Enabled                     |\n| :--------------------------------- | :-------------------------: |\n| CPU                                | ✓                     |\n| Apple Silicon GPU (Linux / Asahi)  | ✓                     |\n| Apple Silicon GPU (macOS)          | ✓ llama.cpp or MLX    |\n| Apple Silicon GPU (podman-machine) | ✓                     |\n| Nvidia GPU (cuda)                  | ✓ See note below      |\n| AMD GPU (rocm, vulkan)             | ✓                     |\n| Ascend NPU (Linux)                 | ✓                     |\n| Intel ARC GPUs (Linux)             | ✓ See note below      |\n| Intel GPUs (vulkan / Linux)        | ✓                     |\n| Moore Threads GPU (musa / Linux)   | ✓ See note below      |\n| Windows (with Docker/Podman)       | ✓ Requires WSL2       |","github_created_at":"2024-07-24T19:09:58+00:00","created_at":"2026-07-15T11:19:12.806449+00:00","updated_at":"2026-07-15T11:19:15.490087+00:00","categories":[{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"containers","name":"containers"},{"slug":"cuda","name":"cuda"},{"slug":"hacktoberfest","name":"hacktoberfest"},{"slug":"hip","name":"hip"},{"slug":"inference-server","name":"inference-server"},{"slug":"intel","name":"intel"},{"slug":"llamacpp","name":"llamacpp"}],"trust":{"provenance":{"is_fork":false,"github_id":833306239,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-15T11:19:13.739Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":5,"days_since_push":0,"last_release_at":"2026-06-24T23:45:27Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-15T11:19:14.176Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-15T11:19:13.521Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-07-15T11:19:13.521Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-15T11:19:13.521Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-15T11:19:13.521Z"}}}}