NVIDIA NIM

NVIDIA NIM (NVIDIA Inference Microservices) is a technology framework that delivers popular foundation models as pre-tuned, GPU-optimized inference microservices. 

Designed to streamline deployment and scalability, NIM packages AI models (such as large language models and vision transformers) into containerized services that are ready for production use.  

Each microservice is optimized for performance on NVIDIA GPUs, enabling developers and enterprises to quickly integrate advanced AI capabilities into their applications without extensive infrastructure setup or model tuning. 

NVIDIA NIM is built as a modular ecosystem composed of several integrated technologies and tools that support efficient AI inference. These components work together to simplify deployment, scale performance, and provide flexibility across use cases: 

• Triton Inference Server: A core part of NIM, Triton is a high-performance inference runtime that supports multiple frameworks (such as TensorFlow, PyTorch, and ONNX). It enables dynamic batching, concurrent model execution, and model ensemble, all optimized for NVIDIA GPUs. 

• TensorRT: An inference optimizer and runtime library that accelerates deep learning models for low latency and high throughput. NIM leverages TensorRT to further optimize model performance on supported NVIDIA hardware. 

• REST and gRPC APIs: NIM services are accessible via standard REST or gRPC interfaces, allowing easy integration into any application or service pipeline. These APIs support flexible input/output handling and management of inference workflows. 

• Helm Charts: NIM deployments can be managed and orchestrated in Kubernetes environments using Helm charts. These charts provide configurable templates to deploy NIM services at scale across cloud or on-premises infrastructure. 

• NeMo and BioNeMo Model Packs: These are curated collections of foundation models specifically trained for language (NeMo) and biomedical (BioNeMo) domains. The models are pre-tuned and optimized for inference, enabling plug-and-play use within NIM. 

• NVIDIA NGC Container Registry: All NIM services and model containers are distributed through the NVIDIA GPU Cloud (NGC) registry. This registry ensures secure, version-controlled access to the latest prebuilt microservices and supporting software. 

Together, these components form a production-ready platform that accelerates the deployment of AI applications, particularly in enterprise and research environments. 

At Kruso, we are piloting NVIDIA NIM on customer-managed GPU clusters to deliver scalable, high-performance AI inference capabilities. This allows us to validate real-world workloads using customers’ existing infrastructure while leveraging NIM’s pre-tuned, GPU-optimized foundation models. 

To ensure repeatable and consistent deployments across environments, we use Terraform modules to automate infrastructure provisioning and service setup. This infrastructure-as-code approach enables us to deploy NIM microservices reliably, manage configurations efficiently, and scale deployments according to customer needs—whether on-premises or in the cloud. 

By combining NVIDIA NIM with Terraform and customer GPU clusters, we can accelerate time-to-value for AI solutions while maintaining flexibility, control, and operational efficiency. 

One of the standout features of NVIDIA NIM is its "five-minute path" from model to production. This means that developers can go from selecting a pre-tuned foundation model to running it as a production-grade inference service in just minutes. By packaging models as containerized microservices—already optimized for NVIDIA GPUs — NIM eliminates the need for complex setup, model conversion, or manual tuning. 

Additionally, NIM is designed for maximum portability: it can run anywhere an NVIDIA driver exists. Whether it's a local workstation, an on-premises GPU server, or a cloud-based Kubernetes cluster, NIM provides consistent performance and deployment flexibility across environments. This makes it ideal for organizations looking to scale AI workloads quickly without being locked into a specific platform. 

Our approach to deploying NVIDIA NIM is centered around portability, performance, and scalability, leveraging the full NIM ecosystem to deliver reliable AI inference services across varied infrastructure setups. 

• Portable inference: By using containerized NIM microservices, we ensure that inference workloads are portable and reproducible across different environments—on-premises, in the cloud, or at the edge. As long as an NVIDIA driver is present, the same microservice can run anywhere. 

• Triton inference server: We rely on Triton to manage and optimize model execution. Triton supports multi-framework models and enables features like dynamic batching and concurrent model serving, which significantly boost performance and resource efficiency. 

• TensorRT: For latency-sensitive applications, we integrate TensorRT to maximize inference speed and throughput. It compiles and optimizes models specifically for NVIDIA GPUs, reducing overhead and ensuring low-latency responses. 

• Helm-based deployment: We deploy NIM services using Helm charts, which allow us to manage Kubernetes-based environments with versioned, customizable templates. This simplifies scaling, updates, and operations across customer clusters. 

• GPU-elastic architecture: Our deployments are designed to be GPU-elastic, meaning they can scale up or down based on available GPU resources. This ensures optimal utilization, cost-efficiency, and consistent performance under varying workloads. 

Together, this architecture enables us to deliver fast, flexible, and production-ready AI services tailored to meet your needs while reducing operational complexity.