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📖 How to Find a GPU Hosting Service – a Guide by Viraaj Akuthota

To fine-tune models and create embeddings on large corpuses of qualitative data, a high amount of GPU RAM (VRAM) is required. For example, fine-tuning BERT on a dataset of 15k cases that vary in size creates roughly 100k-200k sequences at a 512 token limit. This requires approximately 140 GB of VRAM. This hardware requirement means such tasks cannot be conducted on most consumer-grade machines. I conducted an exercise to hopefully identify an affordable and relatively easy-to-use cloud compute option. During this search, I faced many difficulties. The benefits and disadvantages of the majority of service providers I reviewed can be found in the table below.


Overall, the production system I landed on is to utilize:
·    PaperSpace's Core using a Windows Server instance to avoid using the terminal as much as possible.
·    Always available Multi-GPU instances, for example, 4 x A6000 Nvidia GPUs with 192 GB VRAM total for roughly $7 USD an hour.
·    Approximately $3 USD per month for 50 GB persistent storage, making offline costs negligible.
·    For Linux users, they have a Python ML template which will save time installing python, packages, cuda, etc.

Before production, I utilise either Google Colab or HuggingFace:
·    For  testing fine-tuning or creating embeddings, I believe Google Colab's  free T4 instance provides the highest amount of VRAM for any free tier.
·    For  testing LLMs, HuggingFace's serverless inference free tier allows you  to utilize a variety of LLMs such as LLAMA 405B. However, the Pro tier  at $9 USD per month increases the rate limit on this inference. I  receive approximately 300 API calls per hour.

Provider Benefits Disadvantages GPU Limit
Amazon EC2
  • Relatively affordable compared to other cloud providers
  • Requires familiarity with AWS
  • Application for quotas is not straightforward and the approval process takes time
  • Essentially unlimited
Amazon Notebooks
  • Easy to set up an ML system
  • Relatively affordable compared to other cloud providers
  • Notebooks are limited to certain GPU sizes, essentially under 100GB VRAM.
  • Even if you have a quota for the underlying resource it will not work for a       notebook
  • Under 100GB VRAM
Microsoft Azure

  • The registration system and console is sufficiently complicated       that I did not utilise this service.
  • Quota application process did not       seem straight forward.
  • Essentially unlimited
Google Cloud

  • Unable to secure access to a high-end GPU as they were ALWAYS unavailable

Google Colab
  • Very easy to use and set up
  • Relatively more expensive
  • Not guaranteed access to the most powerful GPUs that is claimed to be       accessible even with premium services
  • A100 GPU with 40GB VRAM, if available, which is rare
Paperspace Notebooks
  • Very easy to use and set up
  • Multiple ‘free’ GPU availability with unlimited hours at the premium option

  • P5000 15GB VRAM for the free GPU with the premium plan; Single P6000 16GB at the premium plan – $10 per month; A600 x 4 – 180GB at $7.56 an hour in addition to a       monthly Growth plan, which is $40 per month
Paperspace Server/Console
  • Always available multi-GPU instance
  • ML template server instances
  • Easy server setup
  • More expensive than the big players
  • Some of the ML template server instances come with certain issues with libraries
  • Essentially unlimited