Nvidia NIM provides developers with an efficient means of deploying AI applications with accelerated models. NIMs can be self-hosted in a safe and controlled production environment and developers receive free credits to select foundation models from an extensive API catalog.
NIM supports popular generative AI application frameworks like LangChain and LlamaIndex with industry-standard APIs, and developers can quickly prototype their apps using prebuilt containers which deploy in under five minutes on NVIDIA infrastructure such as clouds, data centers and NVIDIA RTX workstations.
What is NIM?
NIM helps enterprises accelerate GenAI deployments by optimizing inference engines to boost application performance. For RAG apps, this means faster response times and better user experiences – giving businesses leveraged these solutions a significant increase in operational capabilities, streamlining decision-making frameworks and unlocking new revenue streams.
NVIDIA NIM provides developers with access to an expansive library of models across domains and modalities, empowering them to explore uncharted territory in generative AI. Developers can take advantage of NVIDIA APIs to access NIM’s model catalog for prototyping their applications quickly. NIM regularly adds AI models for vision retrieval, 3D model production and digital biology applications – as well as domain-specific solutions with NVIDIA CUDA libraries tailored specifically for speech, video processing, medical imaging analysis or drug discovery among others – helping developers discover uncharted territory when it comes to AI!
NIM makes getting started easy. Leveraging self-hosted endpoints, NIM allows enterprises to set up and manage AI workflows quickly without worrying about infrastructure management or security concerns. Furthermore, monitoring tools enable enterprises to detect and address potential issues quickly while NVIDIA GPUs provide optimal performance and scalability – perfect for enterprise deployment of NIM.
NVIDIA NIM helped telco Amdocs automate the deployment of a finely tuned Mixtral-8x7B model for chat Q&A within 20 minutes, leading to significant increases in AI response accuracy, thus meeting customer service quality benchmarks more quickly and exceeding customer expectations.
NVIDIA NIM is built upon NVIDIA AI Enterprise software and contains an extensive set of industry-standard APIs to facilitate various generative AI applications. Furthermore, the platform features an advanced monitoring and reporting tool that gives administrators an overview of GPU-based deployments.
NIM is a set of easy-to-use microservices designed to speed up generative AI deployment in enterprises.
NIMs accelerate the time between proof-of-concept and production for GenAI applications by offering prebuilt containers and Helm charts containing optimized models optimized for various hardware platforms, cloud service providers, and Kubernetes distributions. Enterprises can go from pilot to production quickly while maintaining complete control of their applications and data.
NVIDIA NIM microservices are designed to be fast, scalable, secure and deploy on any infrastructure quickly and securely. Their integration provides a single path to run many open source and NVIDIA AI Foundation models; custom models built using frameworks like TensorRT, vLLM or PyTorch; optimizing inference for NVIDIA GPUs including their installed base of hundreds of million desktops and servers worldwide.
NIM containers automatically detect local hardware configuration and select an optimized model from a registry for inference using NVIDIA TRT engine or vLLM library for inference tasks. NIM also manages versioning so that only approved models are used during inference processes.
Nutanix, the leading hybrid multicloud platform, and NVIDIA are joining forces to make GenAI more accessible for enterprise developers. Together they will integrate NVIDIA’s NIM inference microservices with Nutanix GPT-in-a-Box 2.0 solution and use NIM to speed development and deployment of generative AI apps more quickly.
NVIDIA NIM provides an easy-to-use set of microservices that streamline generative AI inference, cutting deployment times from weeks to minutes. This service includes pre-built containers with NVIDIA inference software such as Triton Inference Server and TensorRT-LLM libraries that allows users to develop and deploy AI apps more rapidly.
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NIM is a set of optimized inference engines.
NIM is designed to optimize AI infrastructure for scalability and performance, minimizing hardware and operational costs while speeding time to market by simplifying integration code integration and speeding deployment of generative AI applications. NIM packs domain-specific CUDA libraries into an optimized container which runs across any cloud or datacenter environment.
NVIDIA NIM is part of NVIDIA AI Enterprise, an end-to-end software platform that facilitates the development and deployment of production-grade generative AI apps. Utilizing GPUs from NVIDIA GPUs are used for running models on desktops or in cloud datacenters for optimal application acceleration and maximum ROI on hardware investments.
Developers can access NIM through an API catalog of accessible microservices, making NVIDIA AI easy to integrate in any workflow. Accessible via any browser, the API catalog makes integrating NVIDIA AI power effortless.
The NIM API catalog features a selection of foundation models ready for integration in applications. NIM APIs support industry use cases such as facial recognition, visual content creation, speech AI and deep learning – they even provide fine-tuning capabilities so developers can tailor each model specifically to their application and workload needs.
NVIDIA NIM microservices can run on an extensive selection of NVIDIA GPUs, from massive H100 GPUs in the cloud to tiny Jetson GPUs at the edge. Furthermore, using NIM allows you to choose the most efficient model for your application and GPU; this can lead to considerable savings in computational resources as well as improved model efficiencies – for instance NVIDIA NIM has been shown to increase token throughput by 3x in text generation applications, translating to faster applications with increased results per resource unit resulting in better user experiences as well as increased revenues.
NIM is a set of prebuilt microservices.
NIM allows developers to quickly build and deploy generative AI applications using a standard set of APIs on an accelerated infrastructure, cutting deployment times from weeks to minutes. It includes over two dozen NVIDIA inference microservices tailored specifically for GPU installation across clouds, data centers, workstations and PCs; additionally it includes over twenty CUDA-X microservices which facilitate retrieval-augmented generation (RAG), guardrails data processing and HPC capabilities.
Kari Briski, NVIDIA VP for Gen AI Software Products told VentureBeat that NIM’s goal is to assist businesses transition from pilot to production by offering stable APIs, continuous optimization and enterprise-grade support through service level agreements. NIM can be deployed on any infrastructure from NVIDIA DGX Cloud systems and workstations through NVIDIA RTX workstations and PCs and integrated with Haystack LangChain LlamaIndex frameworks for example.
Developers using NIM can quickly and easily create interactive virtual humans for customer service, telehealth, education and customer engagement purposes. Companies such as Activ Surgical, SimBioSys and Artisight use NIM to power their healthcare assistant avatars while biotechnology company A-Alpha Bio is using NIM for its protein language model for predicting and engineering protein-to-protein interactions.
NVIDIA’s NIM catalog of CUDA-X microservices, also available on Amazon SageMaker and Google Kubernetes Engine, contains models such as NVIDIA Riva for customizable speech translation AI; cuOpt for routing optimization; and Earth-2 for high resolution climate and weather simulation. NVIDIA promises it is consistently adding more models to this library.
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NIM is a set of self-hosted endpoints.
NIM provides optimized inference engines, industry-standard APIs and AI model support in containers for fast deployment. Additionally, this approach delivers models for specific domains like language processing, voice recognition and video analysis to meet growing demands for specialized solutions with optimized performance. NIM can be deployed anywhere where basic containers run such as Kubernetes servers running Linux-based operating systems and serverless function-as-a-service models.
NVIDIA NIM is a cloud-native platform with built-in security, compliance and control designed to support multiple GPU types across workstations, data centers and PCs, including those from Cisco, Dell Technologies, Hewlett Packard Enterprise (HPE), Lenovo and Supermicro systems as well as systems from VMware Private AI Foundation with NVIDIA, Red Hat OpenShift and Canonical Charmed Kubernetes.
The NIM platform has been widely adopted across industry to accelerate generative AI applications across a range of use cases. Quantiphi is using its CARA AI platform with Llama 3 model as part of their CARA AI platform to accelerate translational research, clinical development and patient care faster for companies; while Activ Surgical, a smart hospital transformation startup, utilizes it for automating medical documentation and care coordination as well as AITEM providing physician-patient encounter summarization and diagnostic services.
NVIDIA is also helping enable a new class of enterprises that want to deploy generative AI inference more efficiently and scalably, including surgical planning, digital biology and drug discovery applications. By taking advantage of NVIDIA NIM they will save significant costs, boost worker productivity and deliver better patient results.
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