TechPrufer
Sign inGet started

Blogs›NVIDIA›Multi-Device AI Inference with TensorRT

Multi-Device AI Inference with TensorRT

Multi-Device AI Inference with TensorRT

1
posts
2026

This feature thread tracks the evolution of enabling high-performance AI inference across multiple GPUs and edge devices. Initial efforts focused on the challenges of scaling generative AI workloads beyond single-GPU memory and compute limits. TensorRT 11.0 introduced native multi-device inference support, leveraging NVIDIA NCCL for distributed collectives and enabling seamless scaling of generative AI pipelines. This includes the implementation of context parallelism strategies like AllGather KV, Ring Attention, and DeepSpeed Ulysses to efficiently handle long-sequence attention workloads, optimizing for compute, memory, and communication overhead. Benchmarks demonstrate significant latency improvements for diffusion-based media generation at extreme context lengths.

2026

Scaling AI Inference Across Multiple GPUs Using NVIDIA TensorRT with Multi-Device Inference Support | NVIDIA Technical Blog

6/25/2026

This post introduces native multi-device inference support in NVIDIA TensorRT 11.0, enabling scalable AI inference across multiple GPUs and edge devices. It details the integration with NVIDIA NCCL for high-throughput distributed collectives and explores context parallelism strategies (AllGather KV, Ring Attention, DeepSpeed Ulysses) for optimizing long-sequence attention workloads. Benchmarks highlight DeepSpeed Ulysses' performance for diffusion models at extreme context lengths.