Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.

Standardize and optimize AI inference across any framework, any GPU or CPU, and any deployment environment.
NVIDIA Triton Inference Server is a sophisticated open-source inference solution designed for modern AI production environments. In 2026, it stands as the industry standard for high-throughput, low-latency model serving across data centers, cloud, and edge. Triton enables teams to deploy, run, and scale trained AI models from any framework (TensorFlow, PyTorch, ONNX, TensorRT, vLLM, and more) on both GPU and CPU. Its architecture is built around a multi-model execution engine that allows concurrent execution of different model types on a single GPU, maximizing hardware utilization. By abstracting the complexities of backend hardware, Triton provides a unified gRPC and HTTP/REST interface for client applications. The 2026 iteration features enhanced support for Large Language Models (LLMs) through deep integration with TensorRT-LLM and vLLM backends, facilitating advanced techniques like continuous batching and PagedAttention. It is the cornerstone of the NVIDIA AI Enterprise suite, providing the necessary reliability for mission-critical applications while remaining accessible through its open-source core for research and standard development.
Automatically aggregates individual inference requests into a single batch within a user-defined latency window.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The search foundation for multimodal AI and RAG applications.
Accelerating the journey from frontier AI research to hardware-optimized production scale.
The Enterprise-Grade RAG Pipeline for Seamless Unstructured Data Synchronization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Allows multiple models, or multiple instances of the same model, to run simultaneously on a single GPU.
Business Logic Scripting (BLS) allows for complex pipelines and preprocessing/postprocessing logic within the server.
Native backend support for optimized LLM inference featuring PagedAttention and KV caching.
Automated tool that runs sweeps across configurations to find the optimal balance of throughput and latency.
Decoupled architecture supporting PyTorch, TensorFlow, ONNX, OpenVINO, and custom C++ backends.
An optional local or Redis-based cache for storing and reusing previous inference results.
Millisecond latency required to analyze millions of transactions concurrently.
Registry Updated:2/7/2026
Cost-efficient serving of high-parameter models with low time-to-first-token.
Ensuring data privacy while processing high-resolution DICOM files on-premise.