If you want to do it at home, ik_llama.cpp has some performance optimizations that make it semi-practical to run a model of this size on a server with lots of memory bandwidth and a GPU or two for offload. You can get 6-10 tok/s with modest hardware workstation hardware. Thinking chews up a lot of tokens though, so it will be a slog.
Hi Simon. I have a Xeon W5-3435X with a 768GB of DDR5 across 8 channels, iirc it's running at 5800MT/s. It also has 7x A4000s, water cooled to pack them into a desktop case. Very much a compromise build, and I wouldn't recommend Xeon sapphire rapids because the memory bandwidth you get in practice is less than half of what you'd calculate from the specs. If I did it again, I'd build an EPYC machine with 12 channels of DDR5 and put in a single rtx 6000 pro blackwell. That'd be a lot easier and probably a lot faster.
There's a really good thread on level1techs about running DeepSeek at home, and everything there more-or-less applies to Kimi K2.
