Nvidia's Project Digits is a 'personal AI supercomputer'

I feel this is bigger than the 5x series GPUs. Given the craze around AI/LLMs, this can also potentially eat into Apple’s slice of the enthusiast AI dev segment once the M4 Max/Ultra Mac minis are released. I sure wished I held some Nvidia stocks, they seem to be doing everything right in the last few years!

>enthusiast AI dev segment

i think it isn't about enthusiast. To me it looks like Huang/NVDA is pushing further a small revolution using the opening provided by the AI wave - up until now the GPU was add-on to the general computing core onto which that computing core offloaded some computing. With AI that offloaded computing becomes de-facto the main computing and Huang/NVDA is turning tables by making the CPU is just a small add-on on the GPU, with some general computing offloaded to that CPU.

The CPU being located that "close" and with unified memory - that would stimulate development of parallelization for a lot of general computing so that it would be executed on GPU, very fast that way, instead of on the CPU. For example classic of enterprise computing - databases, the SQL ones - a lot, if not, with some work, everything, in these databases can be executed on GPU with a significant performance gain vs. CPU. Why it isn't happening today? Load/unload onto GPU eats into performance, complexity of having only some operations offloaded to GPU is very high in dev effort, etc. Streamlined development on a platform with unified memory will change it. That way Huang/NVDA may pull out rug from under the CPU-first platforms like AMD/INTC and would own both - new AI computing as well as significant share of the classic enterprise one.

> these databases can be executed on GPU with a significant performance gain vs. CPU

No, they can’t. GPU databases are niche products with severe limitations.

GPUs are fast at massively parallel math problems, they anren’t useful for all tasks.

>GPU databases are niche products with severe limitations.

today. For the reasons like i mentioned.

>GPUs are fast at massively parallel math problems, they anren’t useful for all tasks.

GPU are fast at massively parallel tasks. Their memory bandwidth is 10x of that of the CPU for example. So, typical database operations, massively parallel in nature like join or filter, would run about that faster.

Majority of computing can be parallelized and thus benefit from being executed on GPU (with unified memory of the practically usable for enterprise sizes like 128GB).

The unified memory is no faster for the GPU than the CPU. So its not 10x the CPU. HBM on a GPU is much faster.