LocalLLaMA
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I don't have a lot of knowledge on the topic but happy to point you in good direction for reference material. I heard about tensor layer offloading first from here a few months ago. In that post is linked another to MoE expert layer offloadingI highly recommend you read through both post. MoE offloading it was based off
The gist of the Tensor Cores strategy is Instead of offloading entire layers with --gpulayers, you use --overridetensors to keep specific large tensors (particularly FFN tensors) on CPU while moving everything else to GPU.
This works because:
You need to figure out which cores exactly need to be offloaded for your model looking at weights and cooking up regex according to the post.
Heres an example of a kobold startup flags for doing this. The key part is the override tensors flag and the regex contained in it
The exact specifics of how you determine which tensors for each model and the associated regex is a little beyond my knowledge but the people who wrote the tensor post did a good job trying to explain that process in detail. Hope this helps.
Damn! Thank you so much. This is very helpful and a great starting point for me to mess about to make the most of my LLM setup. Appreciate it!!
Late reply, but if you are looking into this, ik_llama.cpp is explicitly optimized for expert offloading. I can get like 16 t/s with a Hunyuan 70B on a 3090.
If you want long context for models that fit in veam your last stop is TabbyAPI. I can squeeze in 128K context from a 32B in 24GB VRAM, easy… I could probably do 96K with 2 parallel slots, though unfortunately most models are pretty terrible past 32K.
I need to mess with tabbyapi. Doesn't help that there's like 2 tabbys, one is tabbyapi and the other is tabbyml. I am guessing tool support is at its infancy stage.
Tabby supports tool usage. It's all just prompting to the underlying LLM, so you can get some frontend to hit the API and do whatever is needed, but I think it does have some kind of native prompt wrapper too.
It is confusing because there are 2 TabbyAPI formats now: exl2 (optimal around 4-5bpw), older and more mature (but now unsupported), and exl3, optimal down to ~3bpw (and usable even below), but slower on some GPUs.