11–20 of 92 posts
Seeing a lot of these “run 1TB models with 1GB RAM” projects recently. Most seem vibe coded and probably won’t be maintained. Hoping a winner emerges with some real momentum behind it.
I’m still slightly confused on what this adds. Let’s say I wanted to run a full size open weight model. I have a 128GB m3 max laptop. Does this basically load layers in and out on demand? So I still have to download the full model to disk, but the RAM requirements go way down? The readme calls out that one still needs to connect HuggingFace, which leads me to believe that maybe you don’t even need to download the ful…
If you point it at a huggingface model identifier, it will download it, I assume. No way around that. It reads like it is keeping only the core and the active layer loaded at any one point, and streams layers from disk; there are several other solutions like this and if my understanding is right, this is probably better than an mmap implementation or just streaming experts in.
It also requires extra space because of decomposition of the layers. Normally the file format optimized for compute intense workloads. But here the bottleneck is the memory capacity.
Also guessing that you need to be able to hold at least 3-layers at once in the memory, given M x N = R operation, M is the previous layer, N is next, and R is the result. on the next "layer", the R (result) becomes M, gets computed against the next layer, N, yielding the further result R'. And so on, until all layers are processed.
I assume it's horribly slow, but can be put in a non-intrusive background task...
I’m still slightly confused on what this adds. Let’s say I wanted to run a full size open weight model. I have a 128GB m3 max laptop. Does this basically load layers in and out on demand? So I still have to download the full model to disk, but the RAM requirements go way down? The readme calls out that one still needs to connect HuggingFace, which leads me to believe that maybe you don’t even need to download the ful…
If you point it at a huggingface model identifier, it will download it, I assume. No way around that. It reads like it is keeping only the core and the active layer loaded at any one point, and streams layers from disk; there are several other solutions like this and if my understanding is right, this is probably better than an mmap implementation or just streaming experts in.
It seems like this tool saves on both disk space and RAM, then. Classic trade off: speed vs space.
For anyone wondering “how slow is this?” IIUC, Kimi K3 on RTX 6000 Ada (48GB) takes 292 s/token https://github.com/lyogavin/airllm/releases/tag/v3.1.0
For anyone wondering “how slow is this?” IIUC, Kimi K3 on RTX 6000 Ada (48GB) takes 292 s/token https://github.com/lyogavin/airllm/releases/tag/v3.1.0
For anyone wondering “how slow is this?” IIUC, Kimi K3 on RTX 6000 Ada (48GB) takes 292 s/token https://github.com/lyogavin/airllm/releases/tag/v3.1.0
For anyone wondering “how slow is this?” IIUC, Kimi K3 on RTX 6000 Ada (48GB) takes 292 s/token https://github.com/lyogavin/airllm/releases/tag/v3.1.0
I do think these “run a bigger model than will fit in VRAM” projects are necessary steps, but are they functionally useful or helpful to anyone currently? For example, is anyone out there running a big Qwen for coding on a 16-32GB machine with these techniques?
Yes and no, depend on your expectations. Some/many like to run local LLMs just for the sake of it, so anything will do.
MoE are useful on PC systems, at the condition of having high enough memory bandwidth (and large amounts of RAM) - that is, Threadripper/Pro.
The advantage of MoE is that only a subset of the model's experts is used for each token, so not all weights need to be present in VRAM at once. The remaining weights can reside in system RAM, although moving and accessing them still carries a substantial performance cost (and that's why high memory bandwidth is needed).
Seeing a lot of these “run 1TB models with 1GB RAM” projects recently. Most seem vibe coded and probably won’t be maintained. Hoping a winner emerges with some real momentum behind it.