DeepSeek's new paper: Storing 100B parameters on CPU RAM
DeepSeek has released a new paper reapplying a classic technique to modern transformer architectures. The goal is to handle 100 billion parameter models using CPU RAM instead of relying exclusively on precious and expensive GPU VRAM.
This approach drastically lowers entry barriers for running giant models by allowing intelligent layer offloading. While pure inference speed cannot compete with an H100 cluster, this technique shifts the bottleneck from video memory capacity to system memory bandwidth, making high-end AI accessible on commodity hardware.
Fabrizio's Analysis:
This is the technical direction I prefer. While everyone is looking for bigger chips, someone is optimizing software to leverage the hardware we already have. I have always maintained that code optimization beats hardware brute force in the long run.
It means we will be able to run complex agents on standard servers or even local workstations without spending a fortune on cloud. For someone like me building agentic architectures, this significantly lowers the TCO (Total Cost of Ownership). Real production latency remains to be seen, but the promise of breaking free from the 'GPU poor' zone is music to my ears.