They actually explained this a few days back (can't seem to find the link right now). But, the core explanation part was it's architecture.

1. MoE (nothing new here, but, this helps a lot)

2. Compressed Attention Mechanisms (this is their core innovation) - this dramatically reduces the Key-Value (KV) cache requirements for longer contexts

Another thing that helps is significantly lower energy costs in China.

Another point from my own guess: they are running (some percentage) the inference on their own home-grown AI inference chips.

Their models are organized around inference efficiency from the start, it's what they're focusing on. Also they come from HFT and are good at low-level optimization. For v3, they've been literally reverse engineering Nvidia GPUs for undocumented behavior that helped against memory bottlenecks, writing file systems for efficient model serving, and doing a ton of low-level grunt work in the times where everyone else just relied on torch. Being compute-constrained helped as well - necessity is the mother of invention.

That is a very good question. It is open source / open weight - yet none of the third party providers, that also host Deepsek, seem to be able to match Deepseek itself on price.

My guess is that they do aggressive caching / some proprietary optimizations in their hosting setup that they haven't published. Maybe also running at loss to gain market share.

And judging from latency / network performance, I don't think what you access, when you access deepseek.com from Europe, is hosted in China.