122B is a more difficult proposition. (Also, keep in mind the 3.6 122B hasn't been released yet and might never be.) With 10B active parameters offloading will be slower - you'd probably want at least 4 channels of DDR5, or 3x 32GB GPUs, or a very expensive Nvidia Pro 6000 Blackwell.
Fedora 43 and LM Studio with Vulkan llama.cpp
An easy way (napkin math) to know if you can run a model based on it's parameter size is to consider the parameter size as GB that need to fit in GPU RAM. 35B model needs atleast 35gb of GPU RAM. This is a very simplified way of looking at it and YES, someone is going to say you can offload to CPU, but no one wants to wait 5 seconds for 1 token.
I used this napkin math for image generation, since the context (prompts) were so small, but I think it's misleading at best for most uses.
Or strix halo.
Seems rather over simplified.
The different levels of quants, for Qwen3.6 it's 10GB to 38.5GB.
Qwen supports a context length of 262,144 natively, but can be extended to 1,010,000 and of course the context length can always be shortened.
Just use one of the calculators and you'll get much more useful number.
You can get tablets, laptops, and desktops. I think windows is more limited and might require static allocation of video memory, not because it's a separate pool, just because windows isn't as flexible.
With linux you can just select the lowest number in bios (usually 256 or 512MB) then let linux balance the needs of the CPU/GPU. So you could easily run a model that requires 96GB or more.
All of them. The static VRAM allocation is tiny (512MB), most of the memory is unified
You can also run those on smaller cards by configuring the number of layers on the GPU. That should allow you to run the Q4/Q5 version on a 4090, or on older cards.
You could also run it entirely on the CPU/in RAM if you have 32GB (or ideally 64GB) of RAM.
The more you run in RAM the slower the inference.
No tuning at all, just apt install rocm and rebuilding llama.cpp every week or so.