We recently released Smaug-72B-v0.1 which has taken first place on the Open LLM Leaderboard by HuggingFace. It is the first open-source model to have an average score more than 80.
I’m afraid to even ask for the minimum specs on this thing, open source models have gotten so big lately
Every billion parameters needs about 2 GB of VRAM - if using bfloat16 representation. 16 bits per parameter, 8 bits per byte -> 2 bytes per parameter.
1 billion parameters ~ 2 Billion bytes ~ 2 GB.
From the name, this model has 72 Billion parameters, so ~144 GB of VRAM
It’s been discovered that you can reduce the bits per parameter down to 4 or 5 and still get good results. Just saw a paper this morning describing a technique to get down to 2.5 bits per parameter, even, and apparently it 's fine. We’ll see if that works out in practice I guess
I’m more experienced with graphics than ML, but wouldn’t that cause a significant increase in computation time, since those aren’t native types for arithmetic? Maybe that’s not a big problem?
If you have a link for the paper I’d like to check it out.
https://huggingface.co/senseable/Smaug-72B-v0.1-gguf/tree/main
About 44GB and 50GB for the Q4 and 5. You’d need quite some extra to fully use the 32k context length.
CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.) To run Qwen-72B-Chat in bf16/fp16, at least 144GB GPU memory is required (e.g., 2xA100-80G or 5xV100-32G). To run it in int4, at least 48GB GPU memory is requred (e.g., 1xA100-80G or 2xV100-32G).
It’s derived from Qwen-72B, so same specs. Q2 clocks it in at only ~30GB.
I think I read somewhere that you’ll basically need 130 GB of RAM to load this model. You could probably get some used server hardware for less than $600 to run this.
Oh if only it were so simple lmao, you need ~130GB of VRAM, aka the graphics card RAM. So you would need about 9 consumer grade 16GB graphics cards and you’ll probably need Nvidia because of fucking CUDA so we’re talking about thousands of dollars. Probably approaching 10k
Ofc you can get cards with more VRAM per card, but not in the consumer segment so even more $$$$$$
Afaik you can substitute VRAM with RAM at the cost of speed. Not exactly sure how that speed loss correlates to the sheer size of these models, though. I have to imagine it would run insanely slow on a CPU.
I’m pretty sure you can load the model using RAM like another poster said. Here’s a used server under $600 that could theoretically run it: ebay.
Unless you’re getting used datacenter grade hardware for next to free, I doubt this. You need 130 gb of VRAM on your GPUs
So can I run it on my Radeon RX 5700? I overclocked it some and am running it as a 5700 XT, if that helps.
i thought this was about the MUD server software, and got excited. alas.
Oh yay another model I can’t run on my computer :'(
That’s nice and all, but what are some FOSS models I can run on GPU with only 4GB?
I’ve tried Deepseek Coder, and it’s pretty nice for what I use it for. Then there’s TinyLlama, which… well it’s fast, but I need to be veeeery exact in how I prompt it.
Unfortunately LLMs need a lot of VRAM. You could try using koboldcpp, it runs on the CPU but let’s you offload layers onto the GPU. That way you might be able to stay withing those 4gb even with larger models.
Edit: I forgot to mention there’s a fork of koboldcpp with rocm for AMD cards, which is about twice as fast if I remember correctly. Only relevant if you have an AMD card tho.
Edit 2: This is the model I use btw
I’m currently playing around with the Jan client, which uses the nitro engine. I think I need to read up on it more, because when I set the ngl value to 15 in order to offload 50% to GPU like the Jan guide says, nothing happens. Though that could be an issue specific to Jan.
Maybe 50% GPU is already using too much VRAM and it crashes. You could try to set it to 0% GPU and see if that works.
4GB is practically nothing in this space. Ideally you want at least 10GB of dedicated vram if you can’t get even more. Keep in mind you’re also probably trying to share that vram with your operating system. So it’s more like ~3GB before you even started.
Kolboldcpp is capable of using both your GPU and CPU together, you might wanna consider that. (Using a feature called layers) There’s a trade-off that occurs between the memory available and the quality of its output and the speed of the calculation.
The model mentioned in this post can be run on the CPU with enough system ram or swap.
If you wanna keep it all on the GPU check out 4bit models. Also there’s been a lot of work into trying to do this with the raspberry Pi. I suspect that their work could help you out here as well.
Depends on your needs. Best look around in !localllama@sh.itjust.works or similar. (I don’t wanna say reddit but r/localLlama is much larger.)
If you’re more into creative writing, maybe look for places that discuss SillyTavern (r/SillyTavernAI is an option). It’s software for role-play chats, which may not be what you want. But the community is (relatively) large and likely to have good tips for non-coding/less technical applications.
Where can we all see the leader board?
Edit: never mind
