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    Price, availability, $ / performance in 2023

    16 July 2023No Comments6 Mins Read

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    If you’re flexible about your GPU model and want to choose the most efficient cloud GPU, check out our cloud GPU $/bandwidth chart.

    If you know a specific cloud GPU (eg A100) you want, name the cloud providers that offer it.

    Cloud GPU cost per bandwidth

    Spot GPUs

    cloud GPU Type / Memory # GPU on the place Bandwidth * Bandwidth* / $***
    Jarvislabs A100 / 40 GB 1 0.69 179 259
    GCP A100 / 40 GB 1 1.1 179 163
    blue A100 / 80 GB 1 1.47 232 157
    blue A100 / 80 GB 4 5.88 821 140
    blue A100 / 80 GB 2 2.94 406 138
    GCP V100 / 16 GB 1 0.84 42 50
    GCP V100 / 16 GB 2 1.68 77 46
    AWS V100 / 16 GB 1 0.92 42 46
    GCP V100 / 16 GB 4 3.36 153 46
    GCP V100 / 16 GB 8 6.72 289 43
    AWS V100 / 16 GB 4 3.67 153 42
    AWS V100 / 16 GB 8 7.34 289 39
    blue V100 / 16 GB 1 1.35 42 31
    blue V100 / 16 GB 2 2.7 77 29
    blue V100 / 16 GB 4 5.4 153 28

    * Training throughput is a good metric to measure GPU relative performance. It measures the number of tokens processed by the GPU per second for a language model (ie bert_base_squad).1 Please note that this bandwidth should be taken into account when comparing different GPUs. The same hardware will have significantly different throughput for your workload because there is significant bandwidth even between LLMs running on the same hardware.2

    *** Does not include cost of storage, network performance, ingress/egress etc. This is just the GPU cost.3

    In all this bandwidth per dollar:

    • Not all possible configurations are listed, including deep learning-oriented configurations similar to AWS options.
    • The western or central regions of the US were used whenever possible
    • These are listed prices for each category, they can be improved

    On-demand GPUs from major technical cloud providers

    cloud GPU Type / Memory # GPU $ on request Bandwidth * Bandwidth* / $***
    blue A100 / 80 GB 1 3.67 232 63
    blue A100 / 80 GB 4 14.69 821 56
    blue A100 / 80 GB 2 7.35 406 55
    GCP A100 / 40 GB 1 3.67 179 49
    blue A100 / 80 GB 8 37.18 1362 37
    AWS A100 / 80 GB 8 40.97 1362 33
    GCP V100 / 16 GB 1 2.95 42 14
    AWS V100 / 16 GB 1 3.06 42 14
    blue V100 / 16 GB 1 3.06 42 14
    GCP V100 / 16 GB 2 5.91 77 13
    GCP V100 / 16 GB 4 11.81 153 13
    AWS V100 / 16 GB 4 12.24 153 13
    blue V100 / 16 GB 2 6.12 77 13
    blue V100 / 16 GB 4 12.24 153 13
    GCP V100 / 16 GB 8 23.63 289 12
    AWS V100 / 16 GB 8 24.48 289 12

    On-demand GPU from other cloud providers

    cloud GPU Type / Memory # GPU $ on request Bandwidth * Bandwidth* / $
    Jarvislabs A100 / 40 GB 1 1.1 179 163
    Lambda A100 / 40 GB 1 1.1 179 163
    Lambda H100 / 80 GB 1 1.99 322 162
    Datacrunch A100 / 80 GB 1 1.85 232 125
    latitude H100 / 8 GB 8 22.42 2693 120
    Lambda A100 / 80 GB 8 12 1362 114
    Datacrunch A100 / 80 GB 4 7.4 821 111
    Datacrunch A100 / 80 GB 2 3.7 406 110
    latitude H100 / 8 GB 4 11.96 1321 110
    Datacrunch A100 / 80 GB 8 14.8 1362 92
    Oblivus Cloud A100 / 80 GB 1 2.55 232 91
    RunPod A100 / 80 GB 8 15.12 1362 90
    Oblivus Cloud A100 / 80 GB 2 5.1 406 80
    Oblivus Cloud A100 / 80 GB 4 10.2 821 80
    Oblivus Cloud A100 / 40 GB 1 2.39 179 75
    paper space A100 / 80 GB 1 3.18 232 73
    Oblivus Cloud A100 / 80 GB 8 20.4 1362 67
    Lambda V100 / 16 GB 8 4.4 289 66
    Oblivus Cloud V100 / 16 GB 1 0.65 42 65
    paper space A100 / 80 GB 4 12.72 821 65
    paper space A100 / 80 GB 2 6.36 406 64
    Oblivus Cloud V100 / 16 GB 2 1.3 77 59
    Oblivus Cloud V100 / 16 GB 4 2.6 153 59
    paper space A100 / 40 GB 1 3.09 179 58
    paper space A100 / 80 GB 8 25.44 1362 54
    exoscale V100 / 16 GB 4 3.32 153 46
    Datacrunch V100 / 16 GB 1 1 42 42
    Datacrunch V100 / 16 GB 2 2 77 39
    Datacrunch V100 / 16 GB 4 4 153 38
    exoscale V100 / 16 GB 2 2.01 77 38
    Datacrunch V100 / 16 GB 8 8 289 36
    exoscale V100 / 16 GB 1 1.38 42 30
    OVHcloud V100 / 16 GB 1 1.97 42 21
    OVHcloud V100 / 16 GB 2 3.94 77 20
    OVHcloud V100 / 16 GB 4 7.89 153 19
    paper space V100 / 16 GB 1 2.3 42 18

    Cloud GPU availability in different clouds

    Enter the desired model in the search field to identify all cloud providers that offer it:

    provider GPU Multi-GPU On Demand $ / One GPU Hour***
    AWS M60 8 GB 1, 2, 4x $1.14
    AWS T4 16 GB 1, 2, 4, 8x $1.20
    AWS A10G 24 GB 1, 4, 8x $1.62
    AWS V100 16 GB 1, 4, 8x $3.06
    AWS V100 32GB 8x $3.90**
    AWS A100 40 GB 8x $4.10**
    AWS A100 80 GB 8x $5.12 **
    CoreWeave Quadro RTX 4000 8 GB 1, 2, 3, 4, 5, 6, 7x $0.35
    CoreWeave RTX A4000 16 GB 1, 2, 3, 4, 5, 6, 7x $0.75
    CoreWeave Quadro RTX 5000 16 GB 1, 2, 3, 4x $0.95
    CoreWeave V100 16 GB 1, 2, 3, 4, 5, 6, 7, 8x $1.00
    CoreWeave RTX A5000 24 GB 1, 2, 3, 4x $1.01
    CoreWeave RTX A40 48 GB 1, 2, 3, 4, 5, 6, 7, 8x $1.68
    CoreWeave RTX A6000 48 GB 1, 2, 3, 4, 5, 6, 7, 8x $1.68
    CoreWeave A100 40 GB 1, 2, 3, 4, 5, 6, 7, 8x $2.46
    CoreWeave A100 80 GB 1, 2, 3, 4, 5, 6, 7, 8x $2.61
    GCP T4 16 GB 1, 2, 4x $0.75
    GCP K80 12 GB 1, 2, 4, 8x $0.85
    GCP P4 8 GB 1, 2, 4x $1.00
    GCP P100 for 16GB 1, 2, 4x $1.86
    GCP V100 16 GB 1, 2, 4, 8x $2.88
    GCP A100 40 GB 1, 2, 4, 8, 16x $3.67
    Jarvis Laboratories Quadro RTX 5000 16 GB 1x $0.49
    Jarvis Laboratories Quadro RTX 6000 24 GB 1x $0.99
    Jarvis Laboratories RTX A5000 24 GB 1x $1.29
    Jarvis Laboratories RTX A6000 48 GB 1x $1.79
    Jarvis Laboratories A100 40 GB 1x $2.39
    Lambda Labs Quadro RTX 6000 24 GB 1, 2, 4x $1.25
    Lambda Labs RTX A6000 48 GB 1, 2, 4x $1.45
    Lambda Labs V100 16 GB 8x $6.80
    Linode Quadro RTX 6000 24 GB 1, 2, 4x $1.50
    blue K80 12 GB 1, 2, 4x $0.90
    blue T4 16 GB 1, 4x $1.20
    blue P40 24 GB 1, 2, 4x $2.07
    blue P100 for 16GB 1, 2, 4x $2.07
    blue V100 32GB 8x $2.75
    blue V100 16 GB 1, 2, 4x $3.06
    blue A100 40 GB 8x $3.40**
    blue A100 80 GB 1, 2, 4x $3.67
    blue A100 80 GB 8x $4,096**
    OVH V100 16 GB 1, 2, 4x $1.79
    OVH V100 32 GB 1, 2, 4x $1.99
    paper space Quadro M4000 8 GB 1x $0.45
    paper space Quadro P4000 8 GB 1, 2, 4x $0.51
    paper space Quadro RTX 4000 8 GB 1, 2, 4x $0.56
    paper space RTX A4000 16 GB 1, 2, 4x $0.76
    paper space Quadro P5000 16 GB 1, 2, 4x $0.78
    paper space Quadro RTX 5000 16 GB 1, 2, 4x $0.82
    paper space Quadro P6000 24 GB 1, 2, 4x $1.10
    paper space RTX A5000 24 GB 1, 2, 4x $1.38
    paper space RTX A6000 48 GB 1, 2, 4x $1.89
    paper space V100 32GB 1, 2, 4x $2.30
    paper space V100 16 GB 1x $2.30
    paper space A100 40 GB 1x $3.09
    paper space A100 80 GB 1, 2, 4, 8x $3.19

    ** Calculated values. This was required when single GPU instances were not available.4 5

    Disclaimer

    Please note that cloud providers are constantly updating their offerings, so there may be updates.

    External links

    1. Deep Learning GPU Benchmarks, Lambda Labs, Received July 15, 2023
    2. Open LLM-Perf Leaderboard Hugging Face Read on July 15, 2023
    3. the-full-stack/website/docs/cloud-gpus, GitHub, retrieved 15 July 2023
    4. The Definitive Guide to Cloud GPU Providers, Paperspace, Received July 15, 2023
    5. CloudOptimizer, CloudOptimizer, Retrieved July 15, 2023

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    Cem has been the chief analyst at AIMultiple since 2017. AIMultiple reports to hundreds of thousands of businesses each month (according to similar websites) including 55% of the Fortune 500.

    Jam’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms such as Deloitte, HPE, and NGOs such as the World Economic Forum and supranational organizations such as the European Commission. You can see more reputable companies and resources referencing AIMultiple.

    Throughout his career, Jam has worked as a tech consultant, tech buyer, and tech entrepreneur. For more than ten years, he advised enterprises on technology solutions at McKinsey & Company and Altman Solon. He also published a McKinsey report on digitization.

    He led technology strategy and acquisitions for the phone company, reporting to the CEO. He also led the commercial growth of deep technology company Hypatos, achieving 7-figure annual recurring revenue and 9-figure valuation from 0 to 2 years. Jam’s work at Hypatos has been covered by leading technology publications like TechCrunch, Business Insider.

    Jam regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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