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Modern mixture-of-experts (MoE) language models face capacity and cost challenges with high-bandwidth memory (HBM). This work proposes using high-bandwidth flash (HBF) with a direct GPU-HBF connection to improve scalable inference efficiency.
Modern mixture-of-experts (MoE) language models face capacity and cost challenges with high-bandwidth memory (HBM). This work proposes using high-bandwidth flash (HBF) with a direct GPU-HBF connection to improve scalable inference efficiency.
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Modern mixture-of-experts (MoE) language models increasingly strain the capacity and cost efficiency of high-bandwidth memory (HBM), as rapidly growing expert weights must be provisioned close to GPUs. High-bandwidth flash (HBF) offers substantially greater capacity, but conventional designs typically deliver...
As MoE models grow, efficiently managing expert weights near GPUs is critical for performance and cost. Leveraging HBF with direct GPU paths could enable larger models without the limitations of HBM capacity.
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