Can my GPU run this LLM?

Calculate how much GPU memory you need & breakdown of where it goes for training/inference of any LLM model with quantization (GGML/bitsandbytes), inference frameworks (vLLM/llama.cpp/HF) & QLoRA.

Link: https://rahulschand.github.io/gpu_poor/

Additions

1. Added autocomplete for ease of use

2. Updated config list with new Huggingface trending models (Llava/Mistral/Trismegistus etc.)

Purpose

I made this to check if you can run a particular LLM on your GPU. Useful to figure out the following

1. What quantization I should use to fit any model on my GPU?
2. What max context length my GPU can handle?
3. What kind of finetuning can I do? Full? LoRA? QLoRA?
4. What max batch size I can use during finetuning?
5. What is consuming my GPU memory? What should I change to fit the LLM on my GPU?

The output is the total vRAM & the breakdown of where the vRAM goes (in MB). It looks like below

{
  "Total": 4000,
  "KV Cache": 1000,
  "Model Size": 2000,
  "Activation Memory": 500,
  "Grad & Optimizer memory": 0,
  "cuda + other overhead":  500
}

Can't we just look at the model size & figure this out?

Finding which LLMs your GPU can handle isn't as easy as looking at the model size because during inference (KV cache) takes susbtantial amount of memory. For example, with sequence length 1000 on llama-2-7b it takes 1GB of extra memory (using hugginface LlamaForCausalLM, with exLlama & vLLM this is 500MB). And during training both KV cache & activations & quantization overhead take a lot of memory. For example, llama-7b with bnb int8 quant is of size ~7.5GB but it isn't possible to finetune it using LoRA on data with 1000 context length even with RTX 4090 24 GB. Which means an additional 16GB memory goes into quant overheads, activations & grad memory.

How to use

Model Name/ID/Size

1. You can either enter the model id of a huggingface model (e.g. meta-llama/Llama-2-7b). Currently I have hardcoded & saved model configs of top 3k most downlaoded LLMs on huggingface.
2. If you have a custom model or your hugginface id isn't available then you can either upload a json config (example) or just enter your model size (e.g. 7 billion for llama-2-7b)

Options

1. Inference: Find vRAM for inference using either HuggingFace implementation or vLLM or GGML
2. Training : Find vRAM for either full model finetuning or finetuning using LoRA (currently I have hardcoded r=8 for LoRA config) or using QLoRA.

Quantization

1. Currently it supports: bitsandbytes (bnb) int8/int4 & GGML (QK_8, QK_6, QK_5, QK_4, QK_2). The latter are only for inference while bnb int8/int4 can be used for both training & inference

Context Len/Sequence Length

1. What is the length of your prompt+new maximum tokens generated. Or for training this is the sequence length of your training data. Batch sizes are 1 for inference & can be specified for training. The option to specify batch sizes for inference needs to be added.

How reliable are the numbers?

The results can vary depending on your model, input data, cuda version & what quant you are using & it is impossible to predict exact values. I have tried to take these into account & make sure the results are within 500MB. Below table I cross-check 3b,7b & 13b model memories given by the website vs. what what I get on my RTX 4090 & 2060 GPUs. All values are within 500MB.

How are the values calculated?

Total memory = model size + kv-cache + activation memory + optimizer/grad memory + cuda etc. overhead

1. Model size = this is your .bin file size (divide it by 2 if Q8 quant & by 4 if Q4 quant).
2. KV-Cache = Memory taken by KV (key-value) vectors. Size = (2 x sequence length x hidden size) per layer. For huggingface this (2 x 2 x sequence length x hidden size) per layer. In training the whole sequence is processed at once (therefore KV cache memory = 0)
3. Activation Memory = In forward pass every operation's output has to be stored for doing .backward(). For example if you do output = Q * input where Q = (dim, dim) and input = (batch, seq, dim) then output of shape (batch, seq, dim) will need to be stored (in fp16). This consumes the most memory in LoRA/QLoRA. In LLMs there are many such intermediate steps (after Q,K,V and after attention, after norm, after FFN1, FFN2, FFN3, after skip layer ....) Around 15 intermediate representations are saved per layer.
4. Optimizer/Grad memory = Memory taken by .grad tensors & tensors associated with the optimizer (running avg etc.)
5. Cuda etc. overhead = Around 500-1GB memory is taken by CUDA whenever cuda is loaded. Also there are additional overheads when you use any quantization (like bitsandbytes). There is not straightforward formula here (I assume 650 MB overhead in my calculations for cuda overhead)

Why are the results wrong?

Sometimes the answers might be very wrong in which case please open an issue here & I will try to fix it.

TODO

1. Add support for exLlama
2. ~Add QLora~ ✅
3. Add way to measure approximste tokens/s you can get for a particular GPU
4. ~Improve logic to get hyper-params from size~ (since hidden layer/intermediate size/number of layers can vary for a particular size) ✅
5. Add AWQ