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

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.

Takeaways

1. 1

   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.

2. 2

   Pairing column-parallel and row-parallel projections in a transformer block can limit communication to one AllReduce per block.

3. 3

   Ring Attention overlaps communication with computation and uses online softmax, avoiding materialization of full-size key and value tensors on each GPU.