Scaling AI Inference Across Multiple GPUs Using NVIDIA TensorRT with Multi-Device Inference Support
Quick Answer
NVIDIA's TensorRT now supports multi-device inference, enabling developers to scale generative AI workloads across multiple GPUs without losing optimizations like kernel fusions and quantization.
Quick Take
NVIDIA's TensorRT now supports multi-device inference, enabling developers to scale generative AI workloads across multiple GPUs without losing optimizations like kernel fusions and quantization. This advancement addresses the growing memory and compute demands of media generation pipelines, ensuring efficient production deployments.
Key Points
- Multi-device inference support enhances scalability for generative AI workloads.
- Optimizations like kernel fusions and quantization remain intact during scaling.
- NVIDIA TensorRT addresses memory and compute limitations of single GPUs.
- Developers can efficiently build media generation pipelines with this support.
Article Excerpt
From source RSS / original summaryGenerative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the... 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… Source
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