Technically correct (tm)
Before you get your hopes up: Anyone can download it, but very few will be able to actually run it.
this post was submitted on 24 Jul 2024
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What’s the resources requirements for the 405B model? I did some digging but couldn’t find any documentation during my cursory search.
Typically you need about 1GB graphics RAM for each billion parameters (i.e. one byte per parameter). This is a 405B parameter model. Ouch.
Edit: you can try quantizing it. This reduces the amount of memory required per parameter to 4 bits, 2 bits or even 1 bit. As you reduce the size, the performance of the model can suffer. So in the extreme case you might be able to run this in under 64GB of graphics RAM.
Typically you need about 1GB graphics RAM for each billion parameters (i.e. one byte per parameter). This is a 405B parameter model.
Or you could run it via cpu and ram at a much slower rate.
Yeah uh let me just put in my 512GB ram stick…
Samsung do make them.
Goodluck finding 512gb of VRAM.
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Finally! My dumb dumb 1TB ram server (4x E5-4640 + 32x32GB DDR3 ECC) can shine.
At work we habe a small cluster totalling around 4TB of RAM
It has 4 cooling units, a m3 of PSUs and it must take something like 30 m2 of space
When the 8 bit quants hit, you could probably lease a 128GB system on runpod.
Can you run this in a distributed manner, like with kubernetes and lots of smaller machines?
According to huggingface, you can run a 34B model using 22.4GBs of RAM max. That's a RTX 3090 Ti.
Hmm, I probably have that much distributed across my network... maybe I should look into some way of distributing it across multiple gpu.
Frak, just counted and I only have 270gb installed. Approx 40gb more if I install some of the deprecated cards in any spare pcie slots i can find.
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405b ain't running local unless you got a proepr set up is enterpise grade lol
I think 70b is possible but I haven't find anyone confirming it yet
Also would like to know specs on whoever did it
I've run quantized 70B models on CPU with 32 gigs but it is very slow
I have a home server with 140 gigs of RAM, it was surprisingly cheap. It's an HP z6 with the 6146 gold xeon processor.
I found a seller who was selling it with a low spec silver and 16 gigs of RAM for like 250 bucks.
Found the processor upgrade for about $120 and spend another $150 on 128gb of second-hand ECC ddr4.
I think the total cost was something like $700 after throwing a couple of 8 TB hard drives in.
I've also placed a Nvidia 4070 in it, which I got doing some horse trading.
How close am I on the specs to being able to run the 70b version?
What's the bus speed of the RAM? You might run it just fine but still bottlenecked there.
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I gonna add some RAM with hope I can split original 70b between GPU and RAM. 8b is great what it is as is
Looks like it should be possible, not sure how much performance hit offloading to RAM will do. Fafo
I regularly run llama3 70b unqantized on two P40s and CPU at like 7tokens/s. It's usable but not very fast.
so there is no way a 24gb and 64gb can run thing?
My specs because you asked:
CPU: Intel(R) Xeon(R) E5-2699 v3 (72) @ 3.60 GHz
GPU 1: NVIDIA Tesla P40 [Discrete]
GPU 2: NVIDIA Tesla P40 [Discrete]
GPU 3: Matrox Electronics Systems Ltd. MGA G200EH
Memory: 66.75 GiB / 251.75 GiB (27%)
Swap: 75.50 MiB / 40.00 GiB (0%)
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What are you asking exactly?
What do you want to run? I assume you have a 24GB GPU and 64GB host RAM?
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As a general rule of thumb, you need about 1 GB per 1B parameters, so you're looking at about 405 GB for the full size of the model.
Quantization can compress it down to 1/2 or 1/4 that, but "makes it stupider" as a result.
This would probably run on a a6000 right?
Edit: nope I think I'm off by an order of magnitude
"an order of magnitude" still feels like an understatement LOL
My 35b models come out at like Morse code speed on my 7800XT, but at least it does work?
When the RTX 9090 Ti comes, anyone who can afford it will be able to run it.
That doesn't sound like much of a change from the situation right now.
So does OSM data. Everyone can download the whole earth but to serve it and provide routing/path planning at scale takes a whole other skill and resources. It's a good thing that they are willing to open source their model in the first place.
Wake me up when it works offline "The Llama 3.1 models are available for download through Meta's own website and on Hugging Face. They both require providing contact information and agreeing to a license and an acceptable use policy, which means that Meta can technically legally pull the rug out from under your use of Llama 3.1 or its outputs at any time."
WAKE UP!
It works offline. When you use with ollama, you don't have to register or agree to anything.
Once you have downloaded it, it will keep on working, meta can't shut it down.
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It's available through ollama already. i am running the 8b model on my little server with it's 3070 as of right now.
It's really impressive for a 8b model
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I'm running 3.1 8b as we speak via ollama totally offline and gave info to nobody.
I was able to set up small one via open webui.
It did ask to make an account but I didn't see any pinging home when I did it.
What am I missing here?
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Yo this is big. In both that it is momentous, and holy shit that’s a lot of parameters. How many GB is this model?? I’d be able to run it if I had an few extra $10k bills lying around to buy the required hardware.
Kind of petty from Zuck not to roll it out in Europe due to the digital services act.. But also kind of weird since it's open source? What's stopping anyone from downloading the model and creating a web ui for Europe users?
Did anyone get 70b to run locally?
If so what, what hardware specs?
Afaik you need about 40GB of vram for a 70b model.
Can't you offload some of it to RAM?
Same requirements, but much slower.
I guess time to buy some ram after spending decade at 16gb
That looks good on paper, but while I find ChatGPT good to create critical thinking, I've found Meta's products (Facebook and Instagram) to be sources of disinformation. That makes me have reservations about Meta's intentions with LLMs. As the article says, the model comes pre-trained, so it's most made up of information gathered by Meta.
Neither Meta nor anyone else is hand-curating their dataset. The fact that Facebook is full of grandparents sharing disinformation doesn't impact what's in their model.
But all LLMs are going to have accuracy issues because they're 1) trained on text written by humans who themselves are inaccurate and 2) designed to choose tokens based on probability rather than any internal logic as to whether an answer is factual.
All LLMs are full of shit. That doesn't mean they're not fun or even useful in some applications, but you shouldn't trust anything they write.
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