{"data":{"slug":"nvidia-tensorrt-llm","name":"TensorRT-LLM","tagline":"Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs","github_url":"https://github.com/NVIDIA/TensorRT-LLM","owner":"NVIDIA","repo":"TensorRT-LLM","owner_avatar_url":"https://avatars.githubusercontent.com/u/1728152?v=4","primary_language":"Python","stars":14645,"forks":2756,"topics":["blackwell","cuda","llm-serving","moe","pytorch"],"archived":false,"github_pushed_at":"2026-09-18T07:58:49+00:00","maintenance_label":"Very active","stars_delta_30d":328,"url":"https://www.graphcanon.com/tools/nvidia-tensorrt-llm","markdown_url":"https://www.graphcanon.com/tools/nvidia-tensorrt-llm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nvidia-tensorrt-llm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nvidia-tensorrt-llm","description":"TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.","homepage_url":"https://nvidia.github.io/TensorRT-LLM","license":"Other","open_issues":1510,"watchers":122,"ai_summary":"TensorRT LLM offers a Python API for defining Large Language Models and supports optimizations for efficient inference on NVIDIA GPUs. It includes components for creating Python and C++ runtimes for performant inference execution.","readme_excerpt":"## Getting Started\n\nTo get started with TensorRT-LLM, visit our documentation:\n\n- [Quick Start Guide](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html)\n    - [Running DeepSeek](./examples/models/core/deepseek_v3)\n- [Installation Guide](https://nvidia.github.io/TensorRT-LLM/installation/index.html)\n- [Supported Hardware, Models, and other Software](https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html)\n- [Benchmarking Performance](https://nvidia.github.io/TensorRT-LLM/performance/performance-tuning-guide/benchmarking-default-performance.html#benchmarking-with-trtllm-bench)\n- [Release Notes](https://nvidia.github.io/TensorRT-LLM/release-notes.html)","github_created_at":"2023-08-16T17:14:27+00:00","created_at":"2026-07-11T10:36:57.350512+00:00","updated_at":"2026-09-20T04:29:48.37616+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":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"blackwell","name":"blackwell"},{"slug":"cuda","name":"cuda"},{"slug":"llm-serving","name":"llm-serving"},{"slug":"moe","name":"moe"},{"slug":"pytorch","name":"pytorch"}],"trust":{"provenance":{"is_fork":false,"github_id":679366051,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:18:42.725Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":9,"days_since_push":0,"last_release_at":"2026-09-18T03:14:53Z","stars_delta_30d":328,"open_issues_delta_30d":-106},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":11,"high_count":0,"last_scan_at":"2026-09-18T08:18:44.643Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:18:41.598Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-18T08:18:41.598Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-09-18T08:18:41.598Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.","If you are working with NVIDIA GPUs and require a Python API for defining and optimizing LLMs.","When you need to create Python and C++ runtimes for orchestrating inference execution in a performant way."],"when_not_to_use":["If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs.","If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool.","When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware."],"source":"enrich:decision_facts","observed_at":"2026-09-18T09:55:46.979Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"TensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference execution."}]}}