NVIDIA’s Triton Inference Server has achieved remarkable performance in the latest MLPerf Inference 4.1 benchmarks, according to the NVIDIA Technical Blog. The server, running on a system with eight H200 GPUs, demonstrated virtually identical performance to NVIDIA’s bare-metal submission on the Llama 2 70B benchmark, highlighting its capability to balance feature-rich, production-grade AI inference with peak throughput performance.

NVIDIA Triton Key Features

NVIDIA Triton is an open-source AI model-serving platform designed to streamline and accelerate the deployment of AI inference workloads in production. Key features include universal AI framework support, seamless cloud integration, business logic scripting, model ensembles, and a model analyzer.

Universal AI Framework Support

Initially launched in 2016 with support for the NVIDIA TensorRT backend, Triton now supports all major frameworks including TensorFlow, PyTorch, ONNX, and more. This broad support allows developers to quickly deploy new models into existing production instances, significantly reducing time to market.

Seamless Cloud Integration

NVIDIA Triton integrates deeply with major cloud service providers, enabling easy deployment in the cloud with minimal or no code required. It supports platforms like OCI Data Science, Azure ML CLI, GKE-managed clusters, and AWS Deep Learning containers, among others.

Business Logic Scripting

Triton allows for the incorporation of custom Python or C++ scripts into production pipelines through business logic scripting, enabling organizations to tailor AI workloads to their specific needs.

Model Ensembles

Model Ensembles enable enterprises to connect pre- and post-processing workflows into cohesive pipelines without programming, optimizing infrastructure costs and reducing latency.

Model Analyzer

The Model Analyzer feature allows experimentation with various deployment configurations, visually mapping these configurations to identify the most efficient setup for production use. It also includes GenA-Perf, a tool designed for generative AI performance benchmarking.

Exceptional Throughput Results at MLPerf 4.1

At MLPerf Inference v4.1, hosted by MLCommons, NVIDIA Triton demonstrated its capabilities on a TensorRT-LLM optimized Llama-v2-70B model. The server achieved performance nearly identical to bare-metal submissions, proving that enterprises can achieve both feature-rich production-grade AI inference and peak throughput performance simultaneously.

MLPerf Benchmark Submission Details

The submission included two scenarios: Offline, where inputs are batch processed, and Server, which mimics real-world production deployments with discrete input requests. The NVIDIA Triton implementation used a gRPC client-server setup, with the server providing a gRPC endpoint to interact with TensorRT-LLM.

Next In-Person User Meetup

NVIDIA announced the next Triton user meetup on September 9, 2024, at the Fort Mason Center For Arts & Culture in San Francisco. The event will focus on new LLM features and future innovations.

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