{"data":{"slug":"minimax-ai-minimax-m1","name":"MiniMax-M1","tagline":"Open-weight large-scale hybrid-attention reasoning model","github_url":"https://github.com/MiniMax-AI/MiniMax-M1","owner":"MiniMax-AI","repo":"MiniMax-M1","owner_avatar_url":"https://avatars.githubusercontent.com/u/194880281?v=4","primary_language":"Python","stars":3172,"forks":283,"topics":["large-language-models","llm","minimax-m1","reasoning-models"],"archived":false,"github_pushed_at":"2025-07-07T11:57:22+00:00","maintenance_label":"Dormant","stars_delta_30d":12,"url":"https://www.graphcanon.com/tools/minimax-ai-minimax-m1","markdown_url":"https://www.graphcanon.com/tools/minimax-ai-minimax-m1.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/minimax-ai-minimax-m1","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=minimax-ai-minimax-m1","description":"MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.","homepage_url":"https://www.minimax.io/","license":"Apache-2.0","open_issues":31,"watchers":25,"ai_summary":"MiniMax-M1 is an open-access, large-scale reasoning model using hybrid attention mechanisms for efficient inference.","readme_excerpt":"## 4. Deployment Guide\n\nDownload the model from HuggingFace repository: \n- [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k)\n- [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k)\n\nFor production deployment, we recommend using [vLLM](https://docs.vllm.ai/en/latest/) to serve MiniMax-M1. vLLM provides excellent performance for serving large language models with the following features:\n- 🔥 Outstanding service throughout performance\n- ⚡ Efficient and intelligent memory management\n- 📦 Powerful batch request processing capability\n- ⚙️ Deeply optimized underlying performance\n\nFor detailed vLLM deployment instructions, please refer to our [vLLM Deployment Guide](./docs/vllm_deployment_guide.md).\nAlternatively, you can also deploy using Transformers directly. For detailed Transformers deployment instructions, you can see our [MiniMax-M1 Transformers Deployment Guide](./docs/transformers_deployment_guide.md).","github_created_at":"2025-06-14T08:54:06+00:00","created_at":"2026-07-07T17:35:53.557676+00:00","updated_at":"2026-08-18T00:02:09.126599+00:00","categories":[{"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":"large-language-models","name":"large language models"},{"slug":"llm","name":"llm"},{"slug":"minimax-m1","name":"minimax-m1"},{"slug":"reasoning-models","name":"reasoning-models"}],"trust":{"provenance":{"is_fork":false,"github_id":1001889935,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T00:02:08.234Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":406,"last_release_at":null,"stars_delta_30d":12,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:06:37.516Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T00:02:08.835Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-18T00:02:08.835Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-18T00:02:08.835Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Free to use under Apache-2.0 license, cost considerations will mainly stem from computing resources when deploying."},"requirements":{"notes":["Deployment is recommended using vLLM for optimal performance and efficient processing.","Transformers can also be used directly for deployment, offering an alternative way to integrate MiniMax-M1."],"min_ram_gb":64,"requires_docker":true},"constraints":{"min_ram_gb":64,"pricing_model":"freemium","requires_docker":true},"when_to_use":["When your project requires an open-weight model with flexible access to weights, allowing you to customize the model without any restrictions.","For projects emphasizing efficient memory management during deployment, especially when serving large-scale reasoning models."],"when_not_to_use":["In scenarios where strict proprietary controls over model weights are necessary, as MiniMax-M1's open-access nature might not comply with such stringent requirements.","If your project focuses on lightweight inference without the need for large-scale hybrid-attention mechanisms; smaller models might offer more efficient deployment options."],"source":"enrich:decision_facts","observed_at":"2026-07-14T19:50:38.809Z"},"constraint_facets":{"min_ram_gb":64,"pricing_model":"freemium","requires_docker":true},"decision_summary":[{"label":"Pricing","value":"freemium - Free to use under Apache-2.0 license, cost considerations will mainly stem from computing resources when deploying."},{"label":"Requirements","value":"Min 64 GB RAM; Requires Docker; Deployment is recommended using vLLM for optimal performance and efficient processing.; Transformers can also be used directly for deployment, offering an alternative way to integrate MiniMax-M1."},{"label":"Adopt for","value":"MiniMax-M1 stands out for its open-access nature and hybrid-attention mechanisms that promise efficient inference capabilities."}]}}