[wetwater]

Are there any good sources that I can read up on estimiating what would be hardware specs required for 7B, 13B, 32B .. etc size If I need to run them locally? I am grad student on budget but I want to host one locally and trying to build a PC that could run one of these models.

[coder543]

"B" just means "billion". A 7B model has 7 billion parameters. Most models are trained in fp16, so each parameter takes two bytes at full precision. Therefore, 7B = 14GB of memory. You can easily quantize models to 8 bits per parameter with very little quality loss, so then 7B = 7GB of memory. With more quality loss (making the model dumber), you can quantize to 4 bits per parameter, so 7B = 3.5GB of memory. There are ways to quantize at other levels too, anywhere from under 2 bits per parameter up to 6 bits per parameter are common.

There is additional memory used for context / KV cache. So, if you use a large context window for a model, you will need to factor in several additional gigabytes for that, but it is much harder to provide a rule of thumb for that overhead. Most of the time, the overhead is significantly less than the size of the model, so not 2x or anything. (The size of the context window is related to the amount of text/images that you can have in a conversation before the LLM begins forgetting the earlier parts of the conversation.)

The most important thing for local LLM performance is typically memory bandwidth. This is why GPUs are so much faster for LLM inference than CPUs, since GPU VRAM is many times the speed of CPU RAM. Apple Silicon offers rather decent memory bandwidth, which makes the performance fit somewhere between a typical Intel/AMD CPU and a typical GPU. Apple Silicon is definitely not as fast as a discrete GPU with the same amount of VRAM.

That's about all you need to know to get started. There are obviously nuances and exceptions that apply in certain situations.

A 32B model at 5 bits per parameter will comfortably fit onto a 24GB GPU and provide decent speed, as long as the context window isn't set to a huge value.

[wruza]

Oh, I have a question, maybe you know.

Assuming the same model sizes in gigabytes, which one to choose: a higher-B lower-bit or a lower-B higher-bit? Is there a silver bullet? Like “yeah always take 4-bit 13B over 8-bit 7B”.

Or are same-sized models basically equal in this regard?

[epolanski]

So, in essence, all AMD does to launch a successful GPU in inference space is to load it with ram?

[faizshah]

Go to r/LocalLLAMA they have the most info. There’s also lots of good YouTube channels who have done benchmarks on Mac minis for this (another good value one with student discount).

Since you’re a student most of the providers/clouds offer student credits and you can also get loads of credits from hackathons.

[disgruntledphd2]

MacBook with 64gb RAM will probably be the easiest. As a bonus, you can train pytorch models on the built in GPU.

It's really frustrating that I can't just write off Apple as evil monopolists when they put out hardware like this.

[notjulianjaynes]

https://www.canirunthisllm.net/

[p_l]

Generally, unquantized - double the number and that's the amount of VRAM in GB you need + some extra, because most models use fp16 weights so it's 2 bytes per parameter -> 32B parameters = 64GB

typical quantization to 4bit will cut 32B model into 16GB of weights plus some of the runtime data, which makes it possibly usable (if slow) on 16GB GPU. You can sometimes viably use smaller quantizations, which will reduce memory use even more.

[regularfry]

You always want a bit of headroom for context. It's a problem I keep bumping into with 32B models on a 24GB card: the decent quants fit, but the context you have available on the card isn't quite as much as I'd like.

[randomNumber7]

Yes. You multiply the number of parameters with the number of bytes per parameter and compare it with the amount of GPU memory (or CPU RAM) you have.