This paper proposes a new architecture for Mixture-of-Experts (MoE) models that balances participation, execution, and materialization costs. Practitioners might care because it can lead to significant performance gains in applications where memory and computational resources are limited.
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This paper presents a new technique to reduce memory usage and speed up inference for large neural networks, allowing them to run on consumer hardware with limited memory. A practitioner might care about this because it enables the deployment of large models in edge devices and reduces the need for expensive storage.