By Erez Schnaider, Technical Product Marketing Manager, ClearML
Introduction
Serving large language models (LLMs) and other foundation models at scale is notoriously complex. From choosing the right model runtime to configuring GPU resources, managing secure access, and setting up authenticated communication with model endpoints, the operational burden is high. NVIDIA NIM microservices aim to simplify this by delivering pre-optimized, production-grade containers for serving AI models. But even with NIM microservices, deployment and ongoing management of these containers can still require significant DevOps effort.
That’s where ClearML comes in. ClearML simplifies the deployment and management of NVIDIA NIMs, and in this post, we’ll show you how our platform makes the NIM-accelerated model serving scalable, manageable, and secure.
What Are NIM Microservices?
NIM microservices, part of NVIDIA AI Enterprise, are containerized, production-ready deployments of AI models, built to simplify inference at scale. Each NIM packages a specific foundation model – such as an LLM, vision model, or speech model – with a pre-configured inference backend ((like NVIDIA Dynamo, NVIDIA TensorRT-LLM, or vLLM), exposing an endpoint through which users can access the model.
The goal of NIM is to reduce the complexity of serving models while ensuring optimal performance on NVIDIA accelerated computing. Instead of fine-tuning runtime parameters or writing serving logic from scratch, developers can pull a NIM container and deploy it with minimal setup. This makes it dramatically easier to integrate inference endpoints into applications and pipelines, while maintaining optimal performance for NVIDIA’s accelerated computing.
What Are Universal LLM NIMs Microservices?
Universal LLM NIM microservices expand on the value of NVIDIA NIM, allowing for deployment of many custom and specialized LLMs.
NIM will add an NVIDIA-maintained container that includes leading inference backends (like TensorRT-LLM, vLLM and SGlang), continuously updated with security patches and tuned for production. This container is paired with external model checkpoints, allowing teams to plug in their choice of LLM.
This architecture provides additional flexibility, allowing for the deployment of thousands of AI models, across domains, with NIM microservices. You can swap, upgrade, or experiment with different models without rebuilding the container. It supports access to more models, faster iteration, and greater flexibility, all while keeping the inference layer secure and highly performant.
By separating model and runtime, Universal NIMs streamline the path to production across a wide range of LLM and foundation model use cases, making enterprise AI deployment more modular, scalable, and efficient.
ClearML + NVIDIA NIM
ClearML takes the power of Universal NIMs and makes them accessible with unmatched simplicity. Our platform provides a seamless method to deploy NIM-accelerated models without the operational overhead typically associated with AI infrastructure. Through the ClearML interface, users can deploy any supported NIM container with just a few clicks – they simply choose the container, select the GPU resource, and hit launch. ClearML handles everything else. It pulls the selected NIM container onto the specified GPU resource – whether it’s running on bare metal, in a VM, or inside a Kubernetes cluster – and exposes its endpoint through the ClearML App Gateway.
ClearML also:
- Manages all networking into and out of deployed endpoints
- Enforces RBAC rules on top of endpoints for secure, tenant-aware access
- Authenticates endpoints access for enhanced security
- Automatically handles horizontal scaling based on demand
- Enables multi-tenant deployments on shared infrastructure
- Continuously monitors all deployed endpoints, visualized in a single, unified dashboard
- Handles resource provisioning
These capabilities turn NIM microservices into a truly production-ready, scalable, and secure model serving solution – fully orchestrated and observable from one place.
Conclusion
NIM microservices streamlines NVIDIA-optimized, production-grade model serving for a broad range of LLMs; ClearML simplifies the process even further. With just a few clicks, your organization can run high-performance AI services on trusted NVIDIA infrastructure, all while maintaining full control, visibility, and security.
If you would like to see this in action, please request a demo.
About ClearML
As the leading infrastructure platform for unleashing AI in organizations worldwide, ClearML is used by more than 2,100 customers to manage GPU clusters and optimize utilization, streamline AI/ML workflows, and deploy GenAI models effortlessly. ClearML is an NVIDIA Partner Network (NPN) member, an NVIDIA DGX-Ready Software Partner, certified for NVIDIA AI Enterprise (NVAIE), and recognized as an NVIDIA Inception Premier Member. We’re trusted by more than 300,000 forward-thinking AI builders and IT teams at leading Fortune 500 companies, enterprises, academia, public sector agencies, and innovative start-ups worldwide.