Find and compare the best Artificial Intelligence software for NVIDIA DeepStream SDK in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for NVIDIA DeepStream SDK on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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TensorFlow
TensorFlow
Free 2 RatingsTensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process. -
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Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.
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NVIDIA Triton Inference Server
NVIDIA
FreeThe NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process. -
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NVIDIA TensorRT
NVIDIA
FreeNVIDIA TensorRT is a comprehensive suite of APIs designed for efficient deep learning inference, which includes a runtime for inference and model optimization tools that ensure minimal latency and maximum throughput in production scenarios. Leveraging the CUDA parallel programming architecture, TensorRT enhances neural network models from all leading frameworks, adjusting them for reduced precision while maintaining high accuracy, and facilitating their deployment across a variety of platforms including hyperscale data centers, workstations, laptops, and edge devices. It utilizes advanced techniques like quantization, fusion of layers and tensors, and precise kernel tuning applicable to all NVIDIA GPU types, ranging from edge devices to powerful data centers. Additionally, the TensorRT ecosystem features TensorRT-LLM, an open-source library designed to accelerate and refine the inference capabilities of contemporary large language models on the NVIDIA AI platform, allowing developers to test and modify new LLMs efficiently through a user-friendly Python API. This innovative approach not only enhances performance but also encourages rapid experimentation and adaptation in the evolving landscape of AI applications. -
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NVIDIA Jetson
NVIDIA
The Jetson platform by NVIDIA stands out as a premier embedded AI computing solution, employed by seasoned developers to craft innovative AI products across a multitude of sectors, while also serving as a valuable resource for students and hobbyists eager to engage in practical AI experimentation and creative endeavors. This versatile platform features compact, energy-efficient production modules and developer kits that include a robust AI software stack, enabling efficient high-performance acceleration. Such capabilities facilitate the deployment of generative AI on the edge, thereby enhancing applications like NVIDIA Metropolis and the Isaac platform. The Jetson family encompasses a variety of modules designed to cater to diverse performance and power efficiency requirements, including models like the Jetson Nano, Jetson TX2, Jetson Xavier NX, and the Jetson Orin series. Each module is meticulously crafted to address specific AI computing needs, accommodating a wide spectrum of projects ranging from beginner-level initiatives to complex robotics and industrial applications, ultimately fostering innovation and development in the field of AI. Through its comprehensive offerings, the Jetson platform empowers creators to push the boundaries of what is possible in AI technology. -
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NVIDIA Metropolis
NVIDIA
NVIDIA Metropolis serves as a comprehensive framework that integrates visual data with artificial intelligence to enhance efficiency and safety in various sectors. By analyzing the vast amounts of data generated by countless sensors, it facilitates seamless retail experiences, optimizes inventory control, supports traffic management in smart urban environments, and improves quality assurance in manufacturing settings, as well as patient care in hospitals. This innovative technology, alongside the robust Metropolis developer ecosystem, empowers organizations to develop, implement, and expand AI and IoT solutions across both edge and cloud environments. Furthermore, it aids in the upkeep and enhancement of urban infrastructure, including parking areas, buildings, and public amenities, while also boosting industrial inspection processes, elevating productivity, and minimizing waste in production lines. In doing so, NVIDIA Metropolis not only drives operational advancements but also contributes to sustainable growth and better resource management across numerous industries.
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