Best AI Infrastructure Platforms for NVIDIA NIM
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Compare the Top AI Infrastructure Platforms that integrate with NVIDIA NIM as of October 2025
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This a list of AI Infrastructure platforms that integrate with NVIDIA NIM. Use the filters on the left to add additional filters for products that have integrations with NVIDIA NIM. View the products that work with NVIDIA NIM in the table below.
What are AI Infrastructure Platforms for NVIDIA NIM?
An AI infrastructure platform is a system that provides infrastructure, compute, tools, and components for the development, training, testing, deployment, and maintenance of artificial intelligence models and applications. It usually features automated model building pipelines, support for large data sets, integration with popular software development environments, tools for distributed training stacks, and the ability to access cloud APIs. By leveraging such an infrastructure platform, developers can easily create end-to-end solutions where data can be collected efficiently and models can be quickly trained in parallel on distributed hardware. The use of such platforms enables a fast development cycle that helps companies get their products to market quickly. Compare and read user reviews of the best AI Infrastructure platforms for NVIDIA NIM currently available using the table below. This list is updated regularly.
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NVIDIA AI Enterprise
NVIDIA
The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise accelerates the data science pipeline and streamlines development and deployment of production AI including generative AI, computer vision, speech AI and more. With over 50 frameworks, pretrained models and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI, while also simplifying AI to make it accessible to every enterprise. The adoption of artificial intelligence and machine learning has gone mainstream, and is core to nearly every company’s competitive strategy. One of the toughest challenges for enterprises is the struggle with siloed infrastructure across the cloud and on-premises data centers. AI requires their environments to be managed as a common platform, instead of islands of compute. -
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NVIDIA AI Data Platform
NVIDIA
NVIDIA's AI Data Platform is a comprehensive solution designed to accelerate enterprise storage and optimize AI workloads, facilitating the development of agentic AI applications. It integrates NVIDIA Blackwell GPUs, BlueField-3 DPUs, Spectrum-X networking, and NVIDIA AI Enterprise software to enhance performance and accuracy in AI workflows. NVIDIA AI Data Platform optimizes workload distribution across GPUs and nodes, leveraging intelligent routing, load balancing, and advanced caching to enable scalable, complex AI processes. This infrastructure supports the deployment and scaling of AI agents across hybrid data centers, transforming raw data into actionable insights in real-time. With the platform, enterprises can process and extract insights from structured or unstructured data, unlocking valuable insights from all available data sources, text, PDF, images, and video. -
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VMware Private AI Foundation
VMware
VMware Private AI Foundation is a joint, on‑premises generative AI platform built on VMware Cloud Foundation (VCF) that enables enterprises to run retrieval‑augmented generation workflows, fine‑tune and customize large language models, and perform inference in their own data centers, addressing privacy, choice, cost, performance, and compliance requirements. It integrates the Private AI Package (including vector databases, deep learning VMs, data indexing and retrieval services, and AI agent‑builder tools) with NVIDIA AI Enterprise (comprising NVIDIA microservices like NIM, NVIDIA’s own LLMs, and third‑party/open source models from places like Hugging Face). It supports full GPU virtualization, monitoring, live migration, and efficient resource pooling on NVIDIA‑certified HGX servers with NVLink/NVSwitch acceleration. Deployable via GUI, CLI, and API, it offers unified management through self‑service provisioning, model store governance, and more.
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