> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/app/cloud-gpu.md).

# Cloud GPU

## GPU Rental

* [Vast.ai](https://console.vast.ai/create)
* [Lambda](https://lambdalabs.com/service/gpu-cloud)
* [CoreWeave](https://coreweave.com/gpu-cloud-pricing)
* [RunPod](https://www.runpod.io/gpu-instance/pricing)
* [Jarvislabs.ai](https://jarvislabs.ai/pricing/#nvidia-ampere-gpu)
* [DataCrunch](https://datacrunch.io/)
* [Fluidstack](https://console2.fluidstack.io/deploy)
* [Genesis Cloud](https://www.genesiscloud.com/pricing)
* [Crusoe Cloud](https://crusoecloud.com/pricing/)
* [Paperspace - Gradient Pricing](https://www.paperspace.com/gradient/pricing) Monthly

## Cloud Comparison

* [CloudOptimizer](https://cloudoptimizer.io/)
* [Cloud GPUs](https://cloud-gpus.com/)

## GPU Rent Price Comparison per hour

1xA100, 40GB, Vast, $0.828-$1.372\
1xA100, 40GB, Jarvis, $1.29\
1xA100, 40GB, Runpod, $0.89\
1xA100, 40GB, Lambda, $1.1\
1xA100, 40GB, Coreweave, $2.06\
1xA100, 40GB, CrusoeCloud, $2.35

1xA100, 80GB, Vast, $0.852\
1xA100, 80GB, Runpod, $2.04\
2xA100, 80GB, Lambda, $2.2\
1xA100, 80GB, Coreweave., $2.21\
1xA100, 80GB, Datacrunch, $2.2\
1xA100, 80GB, Fluidstack, $1.94\
1xA100, 80GB, CrusoeCloud, $2.85

1xA6000, 48GB, Vast, $0.587-$1.001\
1xA6000, 48GB, Jarivs, $0.99\
1xA6000, 48GB, Runpod, $0.74\
1xA6000, 48GB, Lambda, $0.80\
1xA6000, 48GB, Coreweave, $1.28\
1xA6000, 48GB, Datacrunch, $0.99

1xRTX3090, 24GB, Vast, $0.18-$0.353\
1xRTX3090, 24GB, Runpod, $0.39\
1xRTX3090, 24GB, Genesis, $1.30

1xV100, 16GB, Vast, $0.372\
1xV100, 16GB, Runpod, $0.28\
1xV100, 16GB, Coreweave, $0.80\
1xV100, 16GB, Google, $0.74\
1xV100, 16GB, Amazon, $0.918\
1xV100, 16GB, Datacrunch, $0.89\
1xV100, 16GB, Fluidstack. $0.8

## Storage Price per month

Runpod $0.20/GB\
Coreweave $0.04/GB

## Best Bang to Bucks GPU for ML

[GPU Benchmarks for Deep Learning - Lambda](https://lambdalabs.com/gpu-benchmarks)\
[AI-Benchmark](https://ai-benchmark.com/ranking_deeplearning)\
[The Best GPUs for Deep Learning in 2023 — An In-depth Analysis](https://timdettmers.com/2023/01/16/which-gpu-for-deep-learning/)\
[Razer Core X - Thunderbolt™ 3 eGPU](https://www.razer.com/gaming-egpus/razer-core-x) External GPU

* 12 GB RTX 3060 (recommended, cheap, low thermal, great memory) start from Rp6.000.000
* 12 GB A2000 (slightly less powerful than 3060 but only 75w power)
* 12 GB RTX 3060 Ti
* 24 GB RTX 3090 (two times performances of 3060) start form Rp12.000.000
* used Tesla V100 32GB

## GPU Rank

RTX 3090 > Tesla V100 32GB > (RTX 3080 = RTX 6000)

* 1.6: RTX A100 40GB > RTX 4090 > RTX A6000
* 1: RTX 3090 Ti 24 GB = V100 32 GB
* 0.8: RTX 3090 24GB = RTX 3080 Ti 12GB > RTX 3080
* 0.75-0.8: A6000 48GB = RTX 6000 24 GB = A4000
* 0.7 A40 = RTX 2080 Ti
* 0.6: RTX 3070 Ti
* 0.5: GTX 1080 Ti, RTX 2070, RTX 2080
* Tesla P100 > P6000
* 0.45-0.48: RTX 3070, RTX 3060 Ti
* 0.35: GTX 1080, RTX 3060, RTX 3050
* 0.25: GTX 1070

A100 > V100 > T4

## Machine Learning Deployment

* [DataCrunch - A100 80GB GPU Servers](https://datacrunch.io/)
* [Towhee - Out-of-box Pipelines - Towhee](https://towhee.io/pipelines?limit=30\&page=1)
* [Rent GPU Servers for Deep Learning and AI - Vast.ai](https://vast.ai/)
* [Replicate–Reproducible machine learning](https://replicate.com/)
* [Gradient - Use machine learning to make anything.](https://gradient.run/)
* [AutoGluon: AutoML for Text, Image, and Tabular Data—AutoGluon Documentation 0.4.0 documentation](https://auto.gluon.ai/stable/index.html)
* [Parsec–Help Center Home](https://support.paperspace.com/hc/en-us/articles/115002289833-Parsec)
* [codeplea/genann: simple neural network library in ANSI C](https://github.com/codeplea/genann)
* [DerekChia/colab-vscode: ✨ 1-Click Free GPU on VS Code with Google Colab](https://github.com/DerekChia/colab-vscode)
* [Code examples](https://keras.io/examples/)
* [Theory of Machine Learning](https://www.tml.cs.uni-tuebingen.de/teaching/2020_maths_for_ml/)
* [Home - Deploifai](https://deploif.ai/)
* [ELBO AI - ML training made easy and cost effective](https://www.elbo.ai/)