Exactly. They did not have to open up their research up and this is what happens when smart researchers are forced to squeeze performance gains out of existing hardware.

They don't have TPUs or access to the latest Vera Rubin GPUs either to get performance gains for free. All of the optimizations Deepseek have done are in software and it goes down to the PTX assembly level.

Compared to Anthropic who are celebrating in fixing a flickering issue in a terminal app which took months to fix.

> All of the optimizations Deepseek have done are in software and it goes down to the PTX assembly level

DeepSeek are still using NVIDIA (PTX) to train on, but for inference have already transitioned to Huawei Ascend chips, and inference speed is what this paper is addressing.

Anthropic almost certainly also has optimized software down to the assembly level, considering this take-home interview challenge they published: https://github.com/anthropics/original_performance_takehome/... which is all about instruction-level performance optimizations. That they don't prioritize UI fixes just means they consider other things more important.

> Compared to Anthropic who are celebrating in fixing a flickering issue in a terminal app which took months to fix.

It's funny, because if you ran Claude Code on a slow terminal, the cause of the flicker was obvious: They kept dumping the entire history of the chat back into the terminal in a number of situations, and relied on the terminal to them end up in the correct state.

All frontier labs are working down to the PTX level (and lower)