ONNX Runtime
E814638
ONNX Runtime is a high-performance, cross-platform inference engine for running machine learning models in the Open Neural Network Exchange (ONNX) format across a variety of hardware and deployment environments.
All labels observed (1)
| Label | Occurrences |
|---|---|
| ONNX Runtime canonical | 6 |
How this entity was disambiguated
This entity first appeared as the object of triple T9675001 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ONNX Runtime Context triple: [NVIDIA Triton Inference Server, supportsFramework, ONNX Runtime]
-
A.
ONNX
ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models that enables interoperability between different deep learning frameworks and tools.
-
B.
NVIDIA TensorRT
NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
-
C.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
-
D.
OpenVINO
OpenVINO is an open-source toolkit from Intel for optimizing and deploying deep learning inference across a range of hardware platforms, especially Intel CPUs, integrated GPUs, VPUs, and FPGAs.
-
E.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ONNX Runtime Target entity description: ONNX Runtime is a high-performance, cross-platform inference engine for running machine learning models in the Open Neural Network Exchange (ONNX) format across a variety of hardware and deployment environments.
-
A.
ONNX
ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models that enables interoperability between different deep learning frameworks and tools.
-
B.
NVIDIA TensorRT
NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime library designed to accelerate AI models on NVIDIA GPUs in production environments.
-
C.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
-
D.
OpenVINO
OpenVINO is an open-source toolkit from Intel for optimizing and deploying deep learning inference across a range of hardware platforms, especially Intel CPUs, integrated GPUs, VPUs, and FPGAs.
-
E.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
- F. None of above. chosen
Statements (58)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning runtime
ⓘ
machine learning inference engine ⓘ open-source software ⓘ |
| developer | Microsoft ⓘ |
| feature |
ONNX model compatibility
ⓘ
cross-platform support ⓘ graph optimizations ⓘ hardware acceleration ⓘ high-performance inference ⓘ model optimization ⓘ quantization support ⓘ training support ⓘ |
| license | MIT License ⓘ |
| partOf |
ONNX ecosystem
ⓘ
linked to:
ONNX
|
| programmingLanguage |
C
ⓘ
C# ⓘ
linked to:
C# programming language
C++ ⓘ Java ⓘ JavaScript ⓘ Objective-C ⓘ Python ⓘ Swift ⓘ |
| relatedTo | ONNX ⓘ |
| repository | https://github.com/microsoft/onnxruntime ⓘ |
| supportsExecutionProvider |
CPUExecutionProvider
ⓘ
CUDA ⓘ
linked to:
NVIDIA CUDA
CoreML ⓘ
linked to:
Core ML
DirectML ⓘ DmlExecutionProvider ⓘ OpenVINO ⓘ ROCm ⓘ TensorRT ⓘ
linked to:
NVIDIA TensorRT
|
| supportsFormat | ONNX ⓘ |
| supportsHardware |
CPU
ⓘ
FPGA ⓘ GPU ⓘ NPU ⓘ VPU ⓘ |
| supportsLanguageBinding |
C API
ⓘ
C# API ⓘ Java API ⓘ
linked to:
Java Class Library
JavaScript API ⓘ Objective-C API ⓘ Python API ⓘ Swift API ⓘ |
| supportsPlatform |
Android
ⓘ
Azure ⓘ Edge devices ⓘ Linux ⓘ Web ⓘ Windows ⓘ iOS ⓘ macOS ⓘ |
| useCase |
cloud deployment
ⓘ
edge deployment ⓘ on-device AI ⓘ production inference ⓘ |
| website | https://onnxruntime.ai ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: ONNX Runtime Description of subject: ONNX Runtime is a high-performance, cross-platform inference engine for running machine learning models in the Open Neural Network Exchange (ONNX) format across a variety of hardware and deployment environments.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.