Also, datacenter scale devices are almost certainly designed to minimize energy use per operation given comparable latency. You can still compete as an on prem consumer by (1) repurposing your existing hardware, which saves on high CapEx costs, (2) increasing latency, getting your answer computed in a longer time, which probably saves at least some power by design if you can leverage e.g. NPUs, or (3) running smaller or more bespoke models that aren't worthwhile for the bigger players to serve at scale.
There's also a likely gain in serving more requests in parallel, but it may have more to do with successfully amortizing memory access for model weights than any inherent increase in efficiency. Anyway, I've argued in sibling comments that you perhaps can also leverage this on consumer hardware for the special case of DeepSeek V4.