Given that it's a 400B-parameter model, but it's a sparse MoE model with 13B active parameters per token, would it run well on an NVIDIA DGX Spark with 128 GB of unified RAM, or do you practically need to hold the full model in RAM even with sparse MoE?

Even with MoE, holding the model in RAM while individual experts are evaluated in VRAM is a bit of a compromise. Experts can be swapped in and out of VRAM for each token. So RAM <-> VRAM bandwidth becomes important. With a model larger than RAM, that bandwidth bottleneck gets pushed to the SSD interface. At least it's read-only, and not read-write, but even the fastest of SSDs will be significantly slower than RAM.

That said, there are folks out there doing it. https://github.com/lyogavin/airllm is one example.

Can run with mmap() but it is slower. 4-bit quantized there is a decent ratio between the model size and the RAM, with a fast SSD one could try to see how it works. However when a model is 4-bit quantized there is often the doubt that it is not better than an 8-bit quantized model of 200B parameters, it depends on the model, on the use case, ... Unfortunately the street for local inference of SOTA model is being stopped by the RAM prices and the GPU request of the companies, leaving us with little. Probably today the best bet is to buy Mac Studio systems and then run distributed inference (MLX supports this for instance), or a 512 GB Mac Studio M4 that costs, like 13k$.

No.

128GB vram gets you enough space for 256B sized models. But 400B is too big for the DGX Spark, unless you connect 2 of them together and use tensor parallel.