How does the inference throughput (tokens per second) of SMoES-based MoE-VLMs compare to dense models of equal

In performing a Bayesian analysis of astronomical data, two difficult problems often emerge. First, in estimating the parameters of some model for the data, the resulting posterior distribution may be multimodal or exhibit pronounced (curving) degeneracies, which can cause problems for traditional MCMC sampling methods. Second, in selecting between a set of competing models, calculation of the Bayesian evidence for each model is computationally expensive. The nested sampling method introduced by Skilling (2004), has greatly reduced the computational expense of calculating evidences and also pr

Research goal: How does the inference throughput (tokens per second) of SMoES-based MoE-VLMs compare to dense models of equal total parameters on multimodal reasoning benchmarks (e.g., MMMU, MathVista) at 7B and 34B scales under varying batch sizes and sequence lengths?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.