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Cloud GPUs – The Full Stack

Cloud GPUs – The Full Stack

2023-05-21 17:08:35

Coaching and operating neural networks usually requires {hardware} acceleration,
and the most well-liked {hardware} accelerator is the venerable graphics processing unit,
or GPU.

We’ve got assembled cloud GPU vendor pricing all into tables, sortable and filterable to your liking!

We’ve got break up the seller choices into two courses:

  • GPU Cloud Servers, that are long-running (however probably pre-emptible) machines, and
  • Severless GPUs, that are machines that scale-to-zero within the absence of visitors (like an AWS Lambda or Google Cloud Perform)

We welcome your assist in including extra cloud GPU suppliers and preserving the pricing data present.

Please file an issue or make a pull request to this repo, enhancing this file to replace the textual content on this web page or one of many CSV recordsdata to replace the information: cloud-gpus.csv for servers and serverless-gpus.csv for serverless choices.

GPU Cloud Server Comparability


  • GCP doesn’t have GPU “situations” in the identical method that AWS and Azure do. As an alternative, any appropriate machine will be linked to a configuration of GPUs. We’ve got chosen machines which are roughly equal to AWS choices.
  • Areas had been set to be the west or central components of the US. GPU availability will depend on the area.
  • Uncooked knowledge will be present in a csv on GitHub.

All costs are in $/hr.

Serverless GPUs


  • We use the basic definition of “serverless”, courtesy of the original AWS announcement on serverless computing: no server administration, versatile scaling, excessive availability, and no idle capability. We solely embody providers that match this criterion in our choices beneath.
  • Direct worth comparisons are trickier for serverless choices: chilly boot time and autoscaling logic can considerably impression cost-of-traffic. Additionally, some suppliers solely cost for time spent responding to requests, whereas others cost for different time you are utilizing their machines, like booting or between requests (see the Idle time charged? column beneath).
  • A few of the suppliers permit configuration of CPU and RAM sources. We’ve got chosen cheap defaults, usually corresponding to the mounted choices of different suppliers.
  • If you recognize a bit about your anticipated visitors patterns, you need to use this tool to check costs for AWS A100 GPU machines and Banana’s serverless equal. Word that’s is made by the builders of Banana, so could also be biased.
  • Uncooked knowledge will be present in a csv on GitHub.
  • Yow will discover pricing pages for the suppliers right here: Banana, Baseten, Modal, Replicate
  • Serverless GPUs are a more recent know-how, so there are fewer gamers, the main points change shortly, and you may count on bugs/rising pains. Keep frosty!

How do I select a GPU?

This web page is meant to trace and make explorable
the present state of pricing and {hardware} for cloud GPUs.

In order for you recommendation on which machines and playing cards are greatest in your use case,
we advocate
Tim Dettmer’s blog post on GPUs for deep learning.

The entire submit is a tutorial and FAQ on GPUS for DNNs,
however when you simply need the ensuing heuristics for decision-making, see the
“GPU Recommendations” section,
which is the supply of the chart beneath.

Flowchart for quickly selecting an appropriate GPU for your needs, by Tim Dettmers

Flowchart for shortly choosing an applicable GPU in your wants, by Tim Dettmers

GPU Uncooked Efficiency Numbers and Datasheets

Under are the uncooked TFLOPs of the completely different GPUs obtainable from cloud suppliers.

GPU Efficiency Benchmarks

Under are some fundamental benchmarks for GPUs on frequent deep studying duties.

Benchmark of different GPUs on a single ImageNet epoch, by AIME

Benchmark of various GPUs on a single ImageNet epoch, by AIME

See Also

Benchmark of different GPUs on a mix of tasks, by Lambda Labs

Benchmark of various GPUs on a mixture of duties, by Lambda Labs

We’re excited to share this course with you for free.

We’ve got extra upcoming nice content material.
Subscribe to remain updated as we launch it.

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