Find and compare the best Artificial Intelligence software for NVIDIA Triton Inference Server in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for NVIDIA Triton Inference Server on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Vertex AI
Google
Free ($300 in free credits) 666 RatingsVertex AI's Artificial Intelligence (AI), enables businesses to integrate intelligent system into their operations. This transforms the way they interact and analyze data with customers. The platform provides a variety of AI-powered models and tools that help businesses solve complex problems, automate business processes, and gain actionable insight. Vertex AI offers scalable solutions that are tailored to specific requirements. It supports a wide range of industries from finance to healthcare. New customers can receive $300 worth of free credits to try out different AI solutions, and accelerate their digitalization efforts. Vertex AI gives businesses the tools they need to harness the power and innovation of AI. -
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TensorFlow
TensorFlow
Free 2 RatingsOpen source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test. -
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TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.
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Amazon SageMaker
Amazon
Amazon SageMaker is a comprehensive service that empowers developers and data scientists to efficiently create, train, and deploy machine learning (ML) models with ease. By alleviating the burdens associated with the various stages of ML processes, SageMaker simplifies the journey towards producing high-quality models. In contrast, conventional ML development tends to be a complicated, costly, and iterative undertaking, often compounded by the lack of integrated tools that support the entire machine learning pipeline. As a result, practitioners are forced to piece together disparate tools and workflows, leading to potential errors and wasted time. Amazon SageMaker addresses this issue by offering an all-in-one toolkit that encompasses every necessary component for machine learning, enabling quicker production times while significantly reducing effort and expenses. Additionally, Amazon SageMaker Studio serves as a unified, web-based visual platform that facilitates all aspects of ML development, granting users comprehensive access, control, and insight into every required procedure. This streamlined approach not only enhances productivity but also fosters innovation within the field of machine learning. -
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FauxPilot
FauxPilot
FreeFauxPilot, a self-hosted open-source alternative to GitHub Copilot, is a self-hosted open-source alternative. It uses the SalesForce CodeGen model on NVIDIA’s Triton Inference Server, with the FasterTransformer as the backend. Docker is required, as well as an NVIDIA GPU that has enough VRAM and the ability split the model over multiple GPUs, if necessary. Downloading models from Hugging Face, and converting them to FasterTransformer compatible formats is the setup. -
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LiteLLM
LiteLLM
FreeLiteLLM is a versatile tool that streamlines interactions with more than 100 Large Language Models through a unified user interface. It provides both a Proxy Server and a Python SDK to allow developers to seamlessly integrate different LLMs into their applications. The Proxy Server allows for centralized management. It facilitates load balancing and cost tracking across multiple projects. It also provides consistent input/output formats compatible with OpenAI Standards. This setup supports multiple service providers. It provides robust observability through the generation of unique call IDs. This allows for precise tracking and logging between systems. Developers can use pre-defined callbacks in order to log data with various tools. LiteLLM is a powerful tool for enterprise users. It offers advanced features such as Single Sign-On, user management and professional support via dedicated channels like Discord or Slack. -
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Azure Machine Learning
Microsoft
Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported. -
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MXNet
The Apache Software Foundation
The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions. -
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NVIDIA Morpheus
NVIDIA
NVIDIA's Morpheus AI framework is GPU-accelerated and allows developers to create applications that are optimized for filtering, classifying, and processing large volumes of cybersecurity data. Morpheus uses AI to reduce time and costs associated with identifying and capturing threats and taking action. This brings a new level to security to data centers, clouds, and the edge. Morpheus extends the capabilities of human analysts with generative AI, automating real-time analyses and responses. It produces synthetic data for AI models to train that accurately identify risks and run what-if scenario. Morpheus can be downloaded as open-source software from GitHub by developers who are interested in the latest prerelease features and want to build their own. NVIDIA AI enterprise offers unlimited usage across all clouds, access NVIDIA AI experts and long-term support.
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