Scaling State-Space Models on Multiple GPUs with Tensor Parallelism

Ali Nemati4 days ago21 sec read30 views

Researchers have developed a tensor parallelism design to scale selective state space model (SSM) inference across multiple GPUs, addressing memory and performance limitations. This innovation improves batch-request throughput significantly for long-context workloads, offering content creators more efficient deployment options for large language models.

Read the full article at arXiv cs.LG (ML)

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