Overview TensorRT-YOLO deploys YOLO models (YOLOv3 through YOLO26, plus YOLO-World, YOLOE and YOLO-Master) as TensorRT engines. It uses TensorRT plugins for post-processing, CUDA kernels for preprocessing and CUDA Graphs, and covers detect, segment, classify, pose and OBB tasks from C++ (single header) or Python. This repo is the community edition (6.4) with a frozen inference interface; a closed-source pro edition is announced but not yet purchasable.
Who it is for: Engineers deploying YOLO models on NVIDIA GPUs or Jetson from C++ or Python
Strengths Covers detect, segment, classify, pose and OBB with one API per task C++ header-only client, no CUDA or TensorRT linking needed when using the module CUDA preprocessing replicates OpenCV letterbox; most pixel errors are 0 Runs on Linux and Windows, x64 and ARM, with Jetson zero-copy mode Weaknesses NVIDIA GPUs only; requires TensorRT and CUDA Community edition frozen; no new runtime features, pipelining or ensembles Pose, segment and OBB engines need custom plugins built into the engine GPL-3.0 license; docs and tutorials are mainly in Chinese What it needs GPU required Docker Needs CUDA, TensorRT, CMake, pybind11, trtyolo-export, OpenCV (examples) Models: YOLOv3 to YOLO26, YOLO-World, YOLOE, YOLO-Master, YOLO12 Quick answers Is tensorrt-yolo open source? Yes. Its code is on GitHub under the GPL-3.0 license.
Can you run tensorrt-yolo with Docker? Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
Does tensorrt-yolo need a GPU? Yes, it needs a GPU.
Also in Model serving Rank Project Score 1 llama.cpp C/C++ inference engine for GGUF LLMs and VLMs with an OpenAI-compatible server 94 out of 100 score 2 Ollama Local model runner with CLI and REST API for open-weight LLMs known advisory in latest version 92 out of 100 score 3 LocalAI Self-hosted inference server with OpenAI-compatible API and pluggable backends 91 out of 100 score # 4vLLM Inference engine serving LLMs with PagedAttention and an OpenAI-compatible API 88 out of 100 score # 5strata Runs a 125B-parameter MoE model on one consumer GPU plus system RAM 88 out of 100 score
How to choose Pick by hardware first (CPU, NVIDIA, AMD, Apple Silicon) and by model format (GGUF, safetensors). Throughput engines (continuous batching, paged attention) need GPUs; single-user servers do not. An OpenAI-compatible API keeps the rest of your stack portable. Health
Releases 1 in the last 12 monthsLast one 334 days ago
Top contributor 100% of commits19 of 19 in the last 12 months
Security advisories NoneOn GitHub, in the last 12 months
OpenSSF Scorecard Not checked Read from GitHub, deps.dev and OpenSSF on 11 Nov 2026. Bots and coding-agent accounts are left out of the commit counts. Not part of the score: how we check .
License in plain words GPL-3.0 Copyleft
Use it at work Yes
Change it and rebrand it Yes
Sell it or host it for others Yes Your part: If you distribute it, or software built on it, share the full source under GPL-3.0. Running it on your own servers is not distributing.
A plain summary, not legal advice. Read the license on GitHub ↗ (opens in a new tab)
Set it up Needs before it works: CUDA, TensorRT, CMake, pybind11, trtyolo-export, OpenCV (examples).
Bring in an engineer who knows tensorrt-yolo . By the hour for upkeep and fixes, or a fixed price for a build. Our own engineering service; it never changes a rank.
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Then ask: Compare tensorrt-yolo with its top alternatives on Archestack
Share this rank #60 of 65 in Model serving on Archestack, as of 11 Nov 2026.
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