---
title: "Scaling AI Inference Across Multiple GPUs Using NVIDIA TensorRT with Multi-Device Inference Support"
description: "Generative AI media pipelines can exceed the memory and compute capacity of a single GPU, creating a deployment challenge for models that retain TensorRT’s kernel fusions, memory planning, and quantization. TensorRT 11.0 introduces native multi-device inference and IDistCollectiveLayer primitives, while NCCL supplies topology-aware transport across NVLink, NVSwitch, PCIe, and InfiniBand for supported distributed collectives. The article contrasts tensor parallelism, which shards layer weights, with context parallelism, which partitions sequences; it then focuses on AllGather KV, Ring Attention, and DeepSpeed Ulysses for long-sequence media attention. It also describes converting PyTorch pipelines with Torch-TensorRT into C++-deployable engines, benchmarking Cosmos 3 and FLUX.1 on an eight-GPU node, and provides C++ and OpenMPI steps for configuring an eight-rank NCCL deployment."
---

# Scaling AI Inference Across Multiple GPUs Using NVIDIA TensorRT with Multi-Device Inference Support

[NVIDIA](https://yomu.fyi/company/nvidia-developer-blog) · Peter Kisfaludi · Jun 25, 2026

**Type:** Tutorial

## Summary

Generative AI media pipelines can exceed the memory and compute capacity of a single GPU, creating a deployment challenge for models that retain TensorRT’s kernel fusions, memory planning, and quantization. TensorRT 11.0 introduces native multi-device inference and IDistCollectiveLayer primitives, while NCCL supplies topology-aware transport across NVLink, NVSwitch, PCIe, and InfiniBand for supported distributed collectives. The article contrasts tensor parallelism, which shards layer weights, with context parallelism, which partitions sequences; it then focuses on AllGather KV, Ring Attention, and DeepSpeed Ulysses for long-sequence media attention. It also describes converting PyTorch pipelines with Torch-TensorRT into C++-deployable engines, benchmarking Cosmos 3 and FLUX.1 on an eight-GPU node, and provides C++ and OpenMPI steps for configuring an eight-rank NCCL deployment.

## Context

Generative AI workloads, including media generation pipelines, are outgrowing the memory and compute capacity of single GPUs. The stated challenge is scaling inference across multiple devices while retaining TensorRT optimizations such as kernel fusions, memory planning, and quantization. Long-sequence attention is identified as a dominant compute and memory cost in modern generative media pipelines.

## Approach / What changed

The post uses TensorRT 11.0 multi-device inference with NCCL collectives and TensorRT's IDistCollectiveLayer primitives. It explains tensor and context parallelism, focusing on AllGather KV, Ring Attention, and DeepSpeed Ulysses, then describes converting PyTorch models with Torch-TensorRT into TensorRT engines for C++ deployment. It also provides C++ network construction, communicator setup, execution-context binding, and OpenMPI launch steps.

## Takeaways

- Tensor parallelism partitions the weights of individual layers across GPUs and is the natural choice when a layer's weights exceed the memory of one device, independently of sequence length or batch size.
- Pairing column-parallel and row-parallel projections in a transformer block can limit communication to one AllReduce per block.
- Ring Attention overlaps communication with computation and uses online softmax, avoiding materialization of full-size key and value tensors on each GPU.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Deployment](https://yomu.fyi/topic/deployment), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Performance](https://yomu.fyi/topic/performance)

- Source: [NVIDIA](https://developer.nvidia.com/blog/scaling-ai-inference-across-multiple-gpus-using-nvidia-tensorrt-with-multi-device-inference-support)
- Source URL: https://developer.nvidia.com/blog/scaling-ai-inference-across-multiple-gpus-using-nvidia-tensorrt-with-multi-device-inference-support
- Ingested by Yomu: 2026-08-27T14:58:28.822Z

[Read original post](https://developer.nvidia.com/blog/scaling-ai-inference-across-multiple-gpus-using-nvidia-tensorrt-with-multi-device-inference-support)