NVIDIA TensorRT を用いた複数 GPU での AI 推論のスケーリングとマルチデバイス推論サポートの紹介

Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the challenge is scaling across multiple devices without sacrificing the critical optimizations—like kernel fusions, memory planning, and quantization—that NVIDIA TensorRT delivers for production deployments.

Multi-device inference support, a new feature introduced in TensorRT 11.0, brings native high-performance multi-GPU inference to the TensorRT runtime, enabling multi-device production deployments targeting edge devices.

Combining the multi-device inference support in TensorRT with Torch-TensorRT, developers can convert and deploy massive PyTorch models out-of-framework, shattering single-device memory and compute limits.

Download TensorRT 11.0 with multi-device inference support from NVIDIA Developer Portal to unlock native, high-performance multi-device acceleration for your models.

NVIDIA NCCL: The transport layer for distributed inference

The NVIDIA Collective Communications Library (NCCL) provides high-performance multi-GPU and multi-node collective operations powering large-scale model training across thousands of GPUs. NCCL automatically selects the optimal transport for a given topology, abstracting NVIDIA NVLink, NVIDIA NVSwitch, PCIe, and InfiniBand behind a uniform interface. By integrating directly with NCCL, TensorRT inherits this transport optimization for inference workloads, when running multi-device inference. For more information on NCCL, see https://developer.nvidia.com/nccl.

The new multi-device feature covers the full set of NVIDIA NCCL distributed collectives: AllReduce, Broadcast, Reduce, AllGather, ReduceScatter, AlltoAll, Gather, and Scatter.

Parallelism strategies for distributed inference

Distributed inference can be expressed using several parallelism strategies, each with different trade-offs between memory savings, compute scaling, and communication overhead. The most common strategies are tensor parallelism and context parallelism.

Tensor parallelism

In tensor parallelism, the weights of a single layer are partitioned across GPUs. Each GPU computes a shard of the layer’s matrix multiplication and then combines partial results through a collective to produce the full output. This reduces per-device memory weight, making it the natural (and often the only) choice when an individual layer’s weights exceed the memory of a single GPU, independent of the input sequence length or batch size.

In a transformer block, column-parallel projections (for example, QKV and the MLP up-projection) are paired with row-parallel projections (the attention output and the MLP down-projection) so that each block requires only a single AllReduce, keeping communication overhead bounded.

Context parallelism

In context parallelism, the input sequence is partitioned across GPUs along the sequence dimension. Each GPU processes only a slice of the sequence, while collective operations make the global sequence available where needed, such as during attention. Context parallelism is particularly effective for long-sequence workloads, where attention’s quadratic scaling with sequence length makes it the dominant consumer of compute and memory.

It is also an especially natural fit for diffusion and DiT models, whose bidirectional attention sidesteps the load-imbalance issues that arise with causal masks.

Read the Context Parallelism for Scalable Million-Token Inference article for additional details on context parallelism.

NVIDIA TensorRT 11.0 introduces support for the IDistCollectiveLayer primitives required by the various parallelization strategies. The remainder of this post focuses on context parallelism, which directly addresses the dominant cost in modern generative media pipelines: long-sequence attention.

Context parallelism for generative media

Diffusion-based image and video generation pipelines spend a large fraction of their compute and memory budget inside attention blocks operating over long token sequences. A high-resolution image latent or a multi-frame video clip can produce sequences of tens of thousands of tokens per block, and attention scales quadratically with sequence length.

AllGather KV

Context parallelism partitions the sequence across GPUs. Each rank processes a slice of the queries (Q) corresponding to its sequence partition. A straightforward way to implement context parallelism is the AllGather KV approach, where ranks exchange their key (K) and value (V) shards through an AllGather collective before computing local attention, enabling each rank to attend over the full sequence. The result is a per-rank attention output covering the full sequence at the cost of one additional collective per attention block, while the local Q × Kᵀ matrix multiplication shrinks proportionally to the number of ranks.

For video and high-resolution image diffusion, this trade-off compounds favorably across denoising steps. Communication overhead per step remains bounded by the sequence-dimension AllGather, while compute and memory savings apply to every attention layer in every step.

Ring Attention

Context parallelism can be implemented in various ways, each presenting distinct trade-offs.

One potential improvement over the AllGather KV method is Ring Attention, where communication and computation are overlapped. This enables each GPU to process its local Q simultaneously as the K and V continuously stream past in a ring topology. Ring Attention also reduces the memory footprint: using an online softmax, the full-size K and V tensors do not need to be materialized on any GPU. Read the Ring Attention with Blockwise Transformers for Near-Infinite Context article to learn more about Ring Attention.

DeepSpeed Ulysses

For long context (tens of thousands of tokens), an alternative context parallelism implementation approach is DeepSpeed Ulysses. It initially partitions individual samples along the sequence dimension across participating GPUs. Before the attention computation, it employs an all-to-all communication collective on the partitioned Q, K, and V.

This ensures that each GPU receives the full sequence length, but only for a non-overlapping subset of the attention heads, enabling them to compute attention in parallel. Finally, a second all-to-all communication gathers the results across the attention heads while repartitioning them along the sequence dimension. Read more about context parallelism for long context in the article DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models.

Benchmarks: Media generation with context parallelism in C++

The following benchmarks evaluate multi-device TensorRT inference for media generation workloads intended for C++ production deployment. Two representative generative AI pipelines are used: a video generation pipeline based on NVIDIA Cosmos 3 and an image generation pipeline based on FLUX.1.

These pipelines were first authored in PyTorch, then converted out of the framework using Torch-TensorRT to produce NVIDIA TensorRT engines suitable for deployment in C++ inference applications. This workflow enables developers to retain PyTorch as the model development environment while deploying optimized TensorRT engines in production systems.

The benchmarks compare end-to-end latency across different context parallelism strategies: AllGather KV, Ring Attention, and Ulysses. All results were collected on a single node with 8 GPUs.

Video generation with NVIDIA Cosmos 3

The NVIDIA Cosmos model platform is a world foundation model platform, and the Cosmos3-Nano model can generate images, video, audio, and other formats based on multimodal inputs, including text, images, and video. We used the example prompt file for our benchmarks. Based on these benchmarks, Ulysses is the clear winner when a diffusion model has excessively long context lengths (in the order of tens of thousands of input tokens).

Image generation with Flux.1

The FLUX.1-dev model from Black Forest Labs can generate images from text descriptions. We used the prompt: “a beautiful photograph of Mt. Fuji during cherry blossom” for our benchmarks. Based on the benchmarks, the Ulysses strategy is the winner in the case of image generation as well, but it’s worth noting that Ring Attention also scaled well to 4 GPUs.

Getting started using TensorRT with the multi-device feature

TensorRT supports multi-device inference, enabling a single network to execute across multiple GPUs through integrated distributed communication primitives. The core workflow is similar to that of single-device TensorRT. The difference is that the network can now include distributed communication layers.

In this guide, it’s assumed that the same network is deployed on all GPU ranks, but this isn’t a strict requirement, and, in theory, each rank can run a different model.

A working sample is provided in the TensorRT repository. The following guide provides a step-by-step description of how to use the new multi-device feature.

Prerequisites

• Download TensorRT 11 from the NVIDIA Developer Portal.

• Install TensorRT 11 following these instructions.

• Get a single-node, multi-GPU machine.

• Install OpenMPI in your chosen development environment (bare metal or in a container)

• Create a network for multi-device inference

At the network level, multi-device inference is enabled through IDistCollectiveLayer for cross-GPU communication. Collective operations can be added directly to a TensorRT network using INetworkDefinition::addDistCollective:

using namespace nvinfer1;

auto network =

std::unique_ptr<INetworkDefinition>(builder->createNetworkV2(

1U << static_cast<uint32_t>(kSTRONGLY_TYPED)));

auto* input =

network->addInput("input", DataType::kFLOAT, Dims2{3, 4});

ITensor& inputTensor = *network->getInput(0);

auto* collectiveLayer = network->addDistCollective(

inputTensor,

CollectiveOperation::kALL_REDUCE,

ReduceOperation::kSUM,

-1,

nullptr,

0

);

collectiveLayer->setNbRanks(8);

For redu