How does the inference throughput of AnyExperts' sparse MoE architecture compare to dense transformers of equi

Abstract

Abstract: We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without ref

Research Question

How does the inference throughput of AnyExperts' sparse MoE architecture compare to dense transformers of equivalent parameter count on code generation benchmarks (e.g., HumanEval, MBPP) when expert count is scaled from 8 to 64?

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