Qwen 27B maxes out at a 16GB context. A nice thing about DeepSeek V4, especially Flash, is that its context size stays tiny even at 1M tokens! Which in turn opens up wide batching on common consumer platforms.
You need wider batches to get effective reuse of experts in any given layer, but you absolutely can. DeepSeek V4 has tiny KV caches that make this quite feasible. When targeting consumer platforms that only have a limited amount of compute headroom to begin with, the approach is quite reasonable.
What do you mean by doing the math? If you repeatedly sample n_active experts out of n_total, why wouldn't you expect to get some meaningful probability of reuse/overlap once your batch grows past size 5 or so (for the sparsest MoE models in common use)? And you only need enough reuse to fill the compute headroom which is quite small on consumer platforms (we won't have huge TOPS numbers for the typical integrated GPU in Strix Halo or even the upcoming RTX Spark). Plus if you're a single user running multiple streams in parallel the choice of experts will be highly biased leading to more reuse.