Launch requirements

[maxbaluev]

Who can share what equipment specifications are needed for each of the model sizes?

[ohmygoobness]

bump, would like to know the VRAM requirements of each model

[lewiswatson55]

For interest, tested using my 3090ti w/ 24gb dedicated:

The standard model (inference), immediately got cuda memory issue... Ran model in fp16 and runs! Seems to hover around 15gb (standard, 16fp). Not scientific test though haha.

[nealmcb]

Here is one more anecdotal data point:

Getting started with the Galactica language model - Prog.World says:

The “basic” version consumes about 11 GB of memory.
.... in the “standard” version, our laptop simply ran out of memory

FWIW, they also say:

Galactica currently works with Python versions 3.8 and 3.9. Model installation is not possible with version 3.10 and above. This limitation is currently due to a library requirement prompt source.

[lewiswatson55]

Haven't tested w/o using 16fp. Or had time to do more tests, but standard using 16fp is giving some fantastic results. I'd imagine basic will be similarly good, though.

edit: article looks like a good set up guide tho - mentions couple issues I also had to work out

[nealmcb]

Note that the "base" model works in a free colab notebook, after selecting Runtime/Change runtime time and picking "GPU".

[vladislavivanistsev]

Note that the "base" model works in a free colab notebook, after selecting Runtime/Change runtime time and picking "GPU".

Would you be so kind and share an example of a colab notebook?

[nealmcb]

@vladislavivanistsev Here is an example of a colab notebook that you should be able to run for free with a GPU runtime: galactica on Colab

I also note, from the paper:

For training the largest 120B model, we use 128 NVIDIA A100 80GB nodes. For inference Galactica 120B
requires a single A100 node.

[agisga]

Ran model in fp16 and runs! Seems to hover around 15gb (standard, 16fp). Not scientific test though haha.

My experience is similar: the (standard, 16fp) model runs for me with no issues across two GPUs (one RTX 2080 Ti + one GTX 1080 Ti), using about 15gb total (8gb+7gb). Not scientific test either :) but wanted to mention in case it helps someone.

[hwasiti]

Where do you specify that the model should be fp16 and not fp32?

[hwasiti]

Ah
Seems something like this:

model = gal.load_model("huge", num_gpus=4, dtype='float16')

[kno10]

Please add a column listing the inference memory requirements for the models, so people can easier judge how much GPU RAM they need for the versions.