The wear and tear issue is valid, but modern flash memory has improved significantly in endurance. Plus, the paper's approach seems to focus more on reading from flash rather than writing to it, which should reduce the wear-out problem.

The paper claims a 4-5x increase in inference speed on CPUs and 20-25x on GPUs compared to naive loading approaches. These numbers are impressive, but I'd like to see more real-world benchmarks and comparisons with other memory optimization techniques.

This research might be a breakthrough for edge computing. Devices at the edge often have limited memory, so efficient LLM inference could enable more advanced AI applications in IoT and mobile devices.

I'm a bit skeptical. Flash memory is slower compared to DRAM. While this approach sounds innovative, I wonder how much it actually impacts the overall performance, especially in real-time applications.

While the software optimizations are intriguing, we shouldn't overlook the hardware aspect. Tailoring algorithms to specific hardware characteristics, like flash memory's sequential data access, is a smart move. It shows a trend towards more hardware-aware AI development.

This is a significant advancement for LLM deployment in constrained environments. The balance between flash memory and DRAM could be a game changer for bringing more powerful models to devices with limited resources.

Isn't there a risk of wearing out the flash memory faster with this approach? Flash has limited write cycles, and if data is constantly being swapped in and out, it could lead to faster degradation.

It's not just about speed. This paper introduces techniques like windowing and row-column bundling, which are tailored for flash memory. These could potentially mitigate the speed issues by optimizing data access patterns.

Beyond performance, this approach could democratize access to advanced AI models. By reducing the dependency on high-end hardware, more researchers and startups could experiment with large models without prohibitive costs.