Sure, but I’m just playing around with small quantized models on my laptop with integrated graphics and the RAM was insanely cheap. It just interests me what LLMs are capable of that can be run on such hardware. For example, llama 3.2 3B only needs about 3.5 GB of RAM, runs at about 10 tokens per second and while it’s in no way comparable to the LLMs that I use for my day to day tasks, it doesn’t seem to be that bad. Llama 3.1 8B runs at about half that speed, which is a bit slow, but still bearable. Anything bigger than that is too slow to be useful, but still interesting to try for comparison.

I’ve got an old desktop with a pretty decent GPU in it with 24 GB of VRAM, but it’s collecting dust. It’s noisy and power hungry (older generation dual socket Intel Xeon) and still incapable of running large LLMs without additional GPUs. Even if it were capable, I wouldn’t want it to be turned on all the time due to the noise and heat in my home office, so I’ve not even tried running anything on it yet.

The only time I can remember 16 GB not being sufficient for me is when I tried to run an LLM that required a tad more than 11 GB and I had just under 11 GB of memory available due to the other applications that were running.

I guess my usage is relatively lightweight. A browser with a maximum of about 100 open tabs, a terminal, a couple of other applications (some of them electron based) and sometimes a VM that I allocate maybe 4 GB to or something. And the occasional Age of Empires II DE, which even runs fine on my other laptop from 2016 with 16 GB of RAM in it. I still ordered 32 GB so I can play around with local LLMs a bit more.

WSL 1 is a compatibility layer that lets Linux programs run on the Windows kernel by translating Linux system calls to Windows system calls, so in that sense I understand the name: it’s a Windows subsystem for Linux [compatibility]. It doesn’t use the Linux kernel at all. With WSL 2 they’re using a real Linux kernel in a virtual machine, so there the name doesn’t make much sense anymore.

And it does proper split DNS by default, using the search domains of each interface. That way you can configure a global DNS resolver while still being able to resolve local hostnames and without leaking other queries. I just hope they’ll also add DoH support, which is less likely to be blocked on a corporate network.