Faster, More Accurate AI Inference

NVIDIA Triton Inference Server is an open-source inference serving software that helps standardize AI model deployment and execution in production from all major AI frameworks on any GPU- or CPU-based infrastructure.

NVIDIA Triton Management Service automates the deployment of multiple Triton Inference Server instances in Kubernetes with resource-efficient model orchestration on GPUs and CPUs.

NVIDIA TensorRT is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications. TensorRT can be deployed, run, and scaled with Triton.

TensorRT-LLM is an open-source library for defining, optimizing, and executing large language models (LLM) for inference in production. It maintains the core functionality of FasterTransformer, paired with TensorRT’s Deep Learning Compiler, in an open source Python API to quickly support new models and customizations.

L4 cost-effectively delivers universal, energy-efficient acceleration for video, AI, visual computing, graphics, virtualization, and more. The GPU delivers 120X higher AI video performance than CPU-based solutions, letting enterprises gain real-time insights to personalize content, improve search relevance, and more.

Combining NVIDIA’s full stack of inference serving software with the L40S GPU provides a powerful platform for trained models ready for inference. With support for structural sparsity and a broad range of precisions, the L40S delivers up to 1.7X the inference performance of the NVIDIA A100 Tensor Core GPU.

H100 delivers the next massive leap in NVIDIA’s accelerated compute data center platform, securely accelerating diverse workloads from small enterprise workloads to exascale HPC and trillion-parameter AI in every data center.

Enterprises need a versatile system to handle the largest models and realize the full potential of their inference infrastructure. GH200 Grace Hopper Superchip delivers over 7X the fast-access memory to the GPU compared to traditional accelerated inference solutions and dramatically more FLOPS than CPU inference solutions to address LLMs, recommenders, vector databases, and more.