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NVMEs are much, much slower than RAM. Especially unified/soldered RAM.

To be fair, llama.cpp had this feature for over a year now. It just applies to GGUF.

I got an m3, I will test it on metal and check how it goes

Yeah a ramdisk would probably work wonders. It's a shame Intel optane didn't became a standard, those type of workflows would be amazing for it.

This is exactly what I was wondering

I gave a talk a few years ago at dask summit (conf?) on making the stars align with dask-cudf here. We were helping a customer accelerate log analytics by proving out our stack for nodes that look roughly like: parallel ssd storage arrays (30 x 3 GB/s?) -> GPUDirect Storage -> 4 x 30 GB/s PCIe (?) -> 8 x A100 GPUs, something like that. It'd be cool to see the same thing now in the LLM world, such as a multi-GPU MoE, or even a single-GPU one for that matter!

The marvel cxl 2.0 ddr4 card Serve the Home used for kvcache speed ups. And I am personally looking forward to cxl 3 and memory coherence across my system builds.

https://www.servethehome.com/hyper-scalers-are-using-cxl-to-...

Isn't m.2 storage but DRAM - hopefully, meaning NVMe/PCIe not SATA speed - already exists as Compute Express Link (CXL), just not in this specific m.2 form factor? If only RAM wasn't silly expensive right now, one could use 31GB/s of additional bandwidth per NVMe connector.

yeah, actually I wanted to see if this was possible at all. I managed to get around 3000 tokens/s on a ps2 with classic transformers, since the emotion engine is capable of 32 bit addresses, but it has like 32gb of ram. So I ran into the question of why was that fast and I couldn't get that speed even with small models, and the deal is that the instructions went right of the memory to the gpu and that's the main difference that does when a regular computer does inference: it has to request the instructions to the cpu every time. As I mentioned too, on professional cards you can avoid these problems naturally, since they got instructions precisely for this, but sadly I don't have 30k bucks to spare on a gpu :(

I can imagine a couple scenarios in which a high-quality, large model would be much preferred over lower latency models, primarily when you need the quality.

I didn't really understand the performance table until I saw the top ones were 8B models.

But 5 seconds / token is quite slow yeah. I guess this is for low ram machines? I'm pretty sure my 5950x with 128 gb ram can run this faster on the CPU with some layers / prefill on the 3060 gpu I have.

I also see that they claim the process is compute bound at 2 seconds/token, but that doesn't seem correct with a 3090?

That's slower than just running it off CPU+GPU. I can easily hit 1.5 tokens/s on a 7950X+3090 and a 20480-token context.

Cost wise it does not seem very effective. .5 token / sec (the optimized one) is 3600 tokens an hour, which costs about 200-300 watts for an active 3090+system. Running 3600 tokens on open router @.4$ for llama 3.1 (3.3 costs less), is about $0,00144. That money buys you about 2-3 watts (in the Netherlands).

Great achievement for privacy inference nonetheless.

Are you taking into account energy costs of running a 3090 at 350 watts for a very long time?

The compression analogy is interesting. Another way of looking at it could be fine-tuning as "knowing what to leave out" - a 3B model for example tuned for a narrow task doesn't need the capacity that makes 70B good at many things.

That would be nice to see. Actually I was thinking about getting another 3090 and a mobo upgrade since I'm bottlenecked by pcie3 to tryna run glm 4.7 or 5 at q4_k_m, it should be possible.

This was the experiment itself https://github.com/xaskasdf/ps2-llm

The idea was basically to run a llm on a ps2, then I ran into some problems as the 32mb ram cap with 4mb vram cap; so I had to figure out a way to stream layers on the forward pass. Given that ps2 manages to give instructions directly to the vram that's capable of 32bit addresses, it gave an insane amount of tok/s, then I wondered if I could do the same on my puter

I wonder too, DMA plays a huge role in most older gaming consoles when the CPUs were far more sluggish.

Perhaps that's what made them think to try.

Perhaps the current batch of smart memory cards which on the PS2 I believe have quite complex DMA capabilities to stream from the SD card game data.

Actually is purely bandwidth-bound, the major bottleneck of the whole process, for me in this case, is the B450 mobo I got that's only capable of pcie3 and 1x8 in the pcie lanes for gpu instead of 1x16; so I'm capped until I get an X570 maybe. I should get around twice or triple the tok speed with that upgrade alone

Yeah I’ve often wondered why folks aren’t training two tier MoEs for VRAM + RAM. We already have designs for shared experts so it cannot be hard to implement a router that allocated 10x or 100x as often to “core” experts vs the “nice to have” experts. I suppose balancing during training is tricky but some sort of custom loss on the router layers should work.

I’ve also wondered why the routers aren’t training to be serially consistent so you can predict layers to swap into VRAM a few layers ahead to maximize available bandwidth.

I updated the documentation to provide more info for the patching process, I added the patches themselves too and provided some risk info about the patches

the nvidia open source driver has been modded previously to unlock enterprise paywalled features like p2p gpu comms https://blog.chlc.cc/p/rtx4090-p2p-unlocked and vGPU splitting https://open-iov.org/index.php/VGPU_Unlock

My impression is that that is limited to assets and really needs to fit into the DirectX framework. From what I can tell, the gpu-nvme-direct is mostly similar to https://github.com/enfiskutensykkel/ssd-gpu-dma and https://github.com/ZaidQureshi/bam

Something like this? (Llama 3.1-8B etched into custom silicon delivering 16,000 tok/s, doesn't use much PCIe bandwidth):

- https://taalas.com/the-path-to-ubiquitous-ai/ - https://chatjimmy.ai/

I did it, but with different quantization compressions, It ran into quality issues, I will try to rerun with the same quants if that fixes the issue, but the most that looks unused, its being used by rotating layers that are being swapped by the cpu from the ram itself, that manages to keep layers warm, ready to use while inferencing and discarding already used ones

I'm not sure, but I suspect that LLM weights don't compress all that well. The intuition here is that training an LLM is compression of the training data into the weights, so they are probably very information dense already. Can't squeeze them down much.

Did you even read anything? hahaha