GPU memory vs shared memory

How many GPUs do I need to train a LLM?

How many GPUs do I need to train a LLM?

This is a complicated question in general, but if we assume that you are using FSDP with 

FULL_SHARD, activation checkpointing, and DecoupledLionW, then a good rule of thumb is:

Your total cluster memory in GB should be larger than 12 * N (# billions of params).

E.g. To train a GPT-13B model which has ~13 billion params, 

have at least 12 * 13 = 156 GB of total memory across your GPUs. 

You can accomplish this with 4xA100-40GB, or 2xA100-80GB, etc.

If you run into OOM errors when using small device counts, 

reduce device_train_microbatch_size until it succeeds.

Keep in mind: even though training will work in these minimalist settings, 

you will get much better throughput_per_device 

if you use a larger cluster or devices with higher memory capacity, 

because this will enable you to use larger microbatch sizes.

print gpu memory status in python

*install pynvml

https://pypi.org/project/pynvml/

pip install pynvml

*use below code in python code

from pynvml import *
nvmlInit()
h = nvmlDeviceGetHandleByIndex(0)
info = nvmlDeviceGetMemoryInfo(h)
print(f'total    : {info.total}')
print(f'free     : {info.free}')
print(f'used     : {info.used}')