You can run multiple inferences in parallel on the same set of weights, that's what batching is. Given enough parallelization it can be almost entirely compute-limited, at least for small context (max ~10GB per request apparently, but that's for 1M tokens!)
I just think this potential workflow needs to be tested so that we know if anything breaks or makes it infeasible. Ultimately it would be slow when running any single agent, but you might be working with a huge amount of them in parallel. I view this as potentially a great way of repurposing low-RAM hardware with this specific model.