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Cloud ComputingAccelerator
Updated on May 20, 2025

Cloud GPUs for Deep Learning: Availability& Price / Performance

If you are flexible about the GPU model, identify the most cost-effective cloud GPU based on our benchmark of 10 GPU models in image and text generation & finetuning scenarios.

If you prefer a specific model (e.g. A100), identify the lowest-cost GPU cloud provider offering it.

If undecided between on-prem and the cloud, explore whether to buy or rent GPUs on the cloud.

Or learn our cloud GPU benchmark methodology to identify the most cost-efficient GPU

Cloud GPU price per throughput

Two common pricing models for GPUs are “on-demand” and “spot” instances. See the most cost effective GPU for your workload based on on-demand prices from the top 3 hyperscalers:

Cloud GPU Throughput & Prices

Showing 10 of 75
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GPU Name
Cloud
Region
Runpod

Runpod

Code
NVIDIA L4_1_US-GA-2
Region
North America
GPU
1 x NVIDIA L4 24 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
23
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 0.43
192,558 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Runpod

Runpod

Code
NVIDIA L4_1_EUR-IS-1
Region
North Europe
GPU
1 x NVIDIA L4 24 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
23
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 0.43
192,558 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Runpod

Runpod

Code
NVIDIA L4_2_EUR-IS-1
Region
North Europe
GPU
2 x NVIDIA L4 24 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
46
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 0.86
192,558 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Runpod

Runpod

Code
NVIDIA L4_4_EUR-IS-1
Region
North Europe
GPU
4 x NVIDIA L4 24 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
91
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 1.72
190,465 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Runpod

Runpod

Code
NVIDIA L4_3_EUR-IS-1
Region
North Europe
GPU
3 x NVIDIA L4 24 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
68
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 1.29
189,767 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Google Cloud Platform

Google Cloud Platform

Code
n1-standard-1
Region
North America
GPU
4 x NVIDIA T4 16 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
71
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 1.45
176,276 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Google Cloud Platform

Google Cloud Platform

Code
n1-standard-1
Region
North America
GPU
2 x NVIDIA T4 16 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
35
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 0.75
168,000 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Google Cloud Platform

Google Cloud Platform

Code
n1-standard-1
Region
North America
GPU
1 x NVIDIA T4 16 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
18
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 0.40
162,000 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Latitude.sh

Latitude.sh

Code
g3.h100.small
Region
North America
GPU
1 x NVIDIA H100 80 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
78
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 1.99
141,106 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
Latitude.sh

Latitude.sh

Code
g3.h100.medium
Region
North America
GPU
4 x NVIDIA H100 80 GB
Images/s
Number of images processed per second. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.
311
Price/h
Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost
$ 7.97
140,477 Images / $
Number of images that can be processed at a cost of $1. Excludes system setup time. Average image size is 700x350. GPU usage was maximized with parallel processing to get optimal performance.

See cloud GPU benchmark methodology for details.

On-demand is the most straightforward pricing model where you pay for the compute capacity by the hour or second, depending on what you use with no long-term commitments or upfront payments. These instances are recommended for users who prefer the flexibility of a cloud GPU platform without any up-front payment or long-term commitment. On-demand instances are usually more expensive than spot instances, but they provide guaranteed uninterrupted capacity.

On-demand GPUs from other cloud providers

Updated at 03-11-2025
CloudGPU / Memory*# of GPUsOn-demand $Throughput**Throughput/$***
RunPodA100 / 80 GB815.121,36290
TensorDockA100 / 80 GB41.200821684
Vast.aiV100 / 16 GB80.944289306
Vast.aiV100 / 16 GB20.35877215
TensorDockA100 / 80 GB11.200232193
TensorDockV100 / 16 GB10.22042191
TensorDockA100 / 80 GB11.400232165
JarvislabsA100 / 40 GB11.1179163
LambdaA100 / 40 GB11.1179163
LambdaH100 / 80 GB11.99322162
Crusoe CloudA100 / 80 GB11.650232140
FluidStackA100 / 40 GB11.290179139
FluidStackA100 / 40 GB11.400179128
Vast.aiA100 / 40 GB11.400179128
DatacrunchA100 / 80 GB11.85232125
Crusoe CloudA100 / 80 GB46.600821124
Crusoe CloudA100 / 40 GB11.450179123
Crusoe CloudA100 / 80 GB23.300406123
SeewebRTX A6000 / 48 GB21.480179121
Vast.aiA100 / 80 GB12.000232116
LambdaA100 / 80 GB8121,362114
DatacrunchA100 / 80 GB47.4821111
DatacrunchA100 / 80 GB23.7406110
CoreWeaveA100 / 80 GB12.210232105
CoreWeaveA100 / 80 GB12.210232105
FluidStackA100 / 80 GB12.210232105
FluidStackA100 / 80 GB12.210232105
SeewebA100 / 80 GB12.220232104
Crusoe CloudA100 / 80 GB813.2001,362103
Vast.aiA100 / 80 GB48.000821103
Vast.aiA100 / 80 GB24.000406101
DatacrunchA100 / 80 GB814.81,36292
SeewebA100 / 80 GB48.88082192
Oblivus CloudA100 / 80 GB12.5523291
SeewebA100 / 80 GB24.44040691
VultrA100 / 80 GB12.60423289
CoreWeaveA100 / 40 GB12.06017987
CoreWeaveA100 / 40 GB12.06017987
Crusoe CloudA100 / 80 GB815.6001,36287
Oblivus CloudA100 / 80 GB25.140680
Oblivus CloudA100 / 80 GB410.282180
VultrA100 / 80 GB410.41782179
VultrA100 / 80 GB25.20840678
Latitude.shH100 (80 GB)835.22,69377
CoreWeaveH100 / 80 GB14.25032276
FluidStackH100 / 80 GB14.25032276
Latitude.shH100 (80 GB)417.61,32175
Oblivus CloudA100 / 40 GB12.3917975
ACE CloudA100 / 80 GB13.11023274
Latitude.shH100 (80 GB)14.432273
Paperspace by DigitalOceanA100 / 80 GB13.1823273
FluidStackH100 / 80 GB14.76032268
CoreWeaveH100 / 80 GB14.78032267
Oblivus CloudA100 / 80 GB820.41,36267
LambdaV100 / 16 GB84.428966
ACE CloudA100 / 80 GB26.20040665
Oblivus CloudV100 / 16 GB10.654265
Paperspace by DigitalOceanA100 / 80 GB412.7282165
VultrA100 / 80 GB820.8331,36265
Paperspace by DigitalOceanA100 / 80 GB26.3640664
Latitude.shA100 (80 GB)823.21,36259
Oblivus CloudV100 / 16 GB21.37759
Oblivus CloudV100 / 16 GB42.615359
Paperspace by DigitalOceanA100 / 40 GB13.0917958
Paperspace by DigitalOceanA100 / 80 GB825.441,36254
CoreWeaveV100 / 16 GB10.8004253
CirrascaleA100 / 80 GB826.0301,36252
ExoscaleV100 / 16 GB43.3215346
ACE CloudA100 / 80 GB29.28040644
VultrH100 / 80 GB17.532243
DatacrunchV100 / 16 GB114242
DatacrunchV100 / 16 GB227739
DatacrunchV100 / 16 GB4415338
ExoscaleV100 / 16 GB22.017738
CirrascaleA100 / 80 GB422.96082136
DatacrunchV100 / 16 GB8828936
ExoscaleV100 / 16 GB11.384230
OVHcloudV100 / 16 GB11.974221
OVHcloudV100 / 16 GB23.947720
OVHcloudV100 / 16 GB47.8915319
Paperspace by DigitalOceanV100 / 16 GB12.34218

* Memory and GPU model are not the only parameters. CPUs and RAM can also be important, however, they are not the primary criteria that shape cloud GPU performance. Therefore, for simplicity, we have not included number of CPUs or RAM in these tables.

** Training throughput is a good metric to measure relative GPU effectiveness. It measures the number of tokens processed per second by the GPU for a language model (i.e. bert_base_squad).1 Please note that these throughput values should serve as high level guidelines. The same hardware would have a significantly different throughput for your workload since there is significant throughput difference even between LLMs running on the same hardware.2

*** Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost.3

Spot GPUs

Updated at 08-13-2024
CloudGPU / Memory*# of GPUsSpotThroughput**Throughput/$***
AzureA100 / 80 GB10.76232303
AzureA100 / 80 GB43.05821269
AzureA100 / 80 GB21.53406266
JarvislabsA100 / 40 GB10.79179227
GCPA100 / 40 GB11.62179111
AWSV100 / 16 GB10.924246
AWSV100 / 16 GB43.6715342
AzureV100 / 16 GB11.044240
AWSV100 / 16 GB87.3428939
AzureV100 / 16 GB22.087737
AzureV100 / 16 GB44.1615337

In all these throughput per dollar tables:

  • Not all possible configurations are listed, more commonly used, deep learning focused configurations are included.
  • West or Central US regions were used where possible
  • These are the list prices for each category, high volume buyers can possibly get better pricing

Finally, it is good to clarify what “spot” means. Spot resources are:

Interruptible so users need to keep on recording their progress. For example, Amazon EC2 P3, which provides V100 32 GB, is one of the most frequently interrupted Amazon spot services.4

Offered on a dynamic, market-driven basis. The price for these GPU resources can fluctuate based on supply and demand, and users typically bid on the available spot capacity. If a user’s bid is higher than the current spot price, their requested instances will run.

Cloud GPU costs & availability

Updated at 03-07-2025
GPULowest price (USD/hr)Vendor with the lowest price
Nvidia L4$0.38Seeweb
Nvidia RTX4000$0.38Hetzner, Paperspace by DigitalOcean
AMD 7900XTX$0.39DataCrunch
Nvidia T4G$0.42AWS
Nvidia M4000$0.45Paperspace by DigitalOcean
Nvidia RTX6000$0.50Lambda Labs
Nvidia T4$0.53Azure
Nvidia V100$0.62DataCrunch
Nvidia H100$2.49Lambda Labs
Nvidia A100$1.29DataCrunch

Sorting by lowest price. For other low cost options, you can check out cloud GPU marketplaces.

GPU availability

Input the model that you want in the search box to identify all cloud providers that offer it:

Updated at 08-13-2024
ProviderGPUMulti-GPU$/hour***
AWSM60 8 GB1, 2, 4x1.14
AWST4 16 GB1, 2, 4, 8x1.20
AWSA10G 24 GB1, 4, 8x1.62
AWSV100 16 GB1, 4, 8x3.06
AWSV100 32 GB8x3.90****
AWSA100 40 GB8x4.10****
AWSA100 80 GB8x5.12****
AzureK80 12 GB1, 2, 4x0.90
AzureT4 16 GB1, 4x1.20
AzureP40 24 GB1, 2, 4x2.07
AzureP100 16 GB1, 2, 4x2.07
AzureV100 32 GB8x2.75
AzureV100 16 GB1, 2, 4x3.06
AzureA100 40 GB8x3.40****
AzureA100 80 GB1, 2, 4x3.67
AzureA100 80 GB8x4.10****
GCPT4 16 GB1, 2, 4x0.75
GCPK80 12 GB1, 2, 4, 8x0.85
GCPP4 8 GB1, 2, 4x1.00
GCPP100 16 GB1, 2, 4x1.86
GCPV100 16 GB1, 2, 4, 8x2.88
GCPA100 40 GB1, 2, 4, 8, 16x3.67
OCIA100 40 GB8x4.00
OCIA100 80 GB8x3.05
OCIA10 24 GB1,2,4x2.00
OCIV100 16 GB1,2,4,8x2.95
OCIP100 16 GB1,2x1.275
ACE CloudA2 (16 GB)1, 2x0.59
ACE CloudA30 (32 GB)1, 2x0.95
ACE CloudA100 (80 GB)1, 2x3.11
Alibaba CloudA100 80 GB8x
CirrascaleA100 (80 GB)4, 8x5.74
CirrascaleRTX A6000 (48 GB)8x1.12
CirrascaleRTX A5000 (24 GB)8x0.51
CirrascaleRTX A4000 (16 GB)8x0.34
CirrascaleA40 (48 GB)8x1.44
CirrascaleA30 (24 GB)8x
CirrascaleV100 (32 GB)4, 8x1.92
CirrascaleRTX 6000 (48GB)8x1.18
CoreWeaveH100 (80 GB)1x4.25
CoreWeaveA100 (80 GB)1x2.21
CoreWeaveA100 (40 GB)1x2.06
CoreWeaveV100 (16 GB)1x0.80
CoreWeaveA40 (48 GB)1x1.28
CoreWeaveRTX 6000 (48 GB)1x1.28
CoreWeaveRTX 5000 (24 GB)1x0.77
CoreWeaveRTX 4000 (16 GB)1x0.61
CoreWeaveQuadro RTX 5000 (16 GB)1x0.57
CoreWeaveQuadro RTX 4000 (8 GB)1x0.24
Crusoe CloudA6000 (48 GB)1, 2, 4, 8x0.92
Crusoe CloudA40 (48 GB)1, 2, 4, 8x1.10
Crusoe CloudA100 (80 GB)1, 2, 4, 8x1.45
Crusoe CloudH100 (80 GB)8x
FluidStackH100 (80 GB)1x4.25
FluidStackA100 (80 GB)1x2.21
Jarvis LabsQuadro RTX 5000 16 GB1x0.49
Jarvis LabsQuadro RTX 6000 24 GB1x0.99
Jarvis LabsRTX A5000 24 GB1x1.29
Jarvis LabsRTX A6000 48 GB1x1.79
Jarvis LabsA100 40 GB1x2.39
Lambda LabsQuadro RTX 6000 24 GB1, 2, 4x1.25
Lambda LabsRTX A6000 48 GB1, 2, 4x1.45
Lambda LabsV100 16 GB8x6.8
Latitude.shH100 (80 GB)1, 4, 8x4.40
Latitude.shA100 (80 GB)8x23.2
LeaderGPUA100 (40 GB)
LeaderGPUA10 (24 GB)
LeaderGPUV100 (32 GB)
LinodeQuadro RTX 6000 24 GB1, 2, 4x1.50
OVHV100 32 GB1, 2, 4x1.99
OVHV100 16 GB1, 2, 4x1.79
PaperspaceQuadro M4000 8 GB1x0.45
PaperspaceQuadro P4000 8 GB1, 2, 4x0.51
PaperspaceQuadro RTX 4000 8 GB1, 2, 4x0.56
PaperspaceRTX A4000 16 GB1, 2, 4x0.76
PaperspaceQuadro P5000 16 GB1, 2, 4x0.78
PaperspaceQuadro RTX 5000 16 GB1, 2, 4x0.82
PaperspaceQuadro P6000 24 GB1, 2, 4x1.10
PaperspaceRTX A5000 24 GB1, 2, 4x1.38
PaperspaceRTX A6000 48 GB1, 2, 4x1.89
PaperspaceV100 32 GB1, 2, 4x2.30
PaperspaceV100 16 GB1x2.30
PaperspaceA100 40 GB1x3.09
PaperspaceA100 80 GB1, 2, 4, 8x3.19
SeewebRTXA6000 (48 GB)1, 2, 3, 4, 5x0.74
SeewebRTXA6000 (24 GB)1, 2, 3, 4, 5x0.64
SeewebA30 (24 GB)1, 2, 3, 4, 5x0.64
SeewebL4 (24 GB)1, 2, 3, 4, 5x0.38
SeewebA100 (80 GB)1, 2, 3, 4, 5x2.22
TensorDockA100 (80 GB)1x1.40
TensorDockL40 (40 GB)1x1.05
TensorDockV100 (16 GB)1x0.22
TensorDockA6000 (48 GB)1x0.47
TensorDockA40 (48 GB)1x0.47
TensorDockA5000 (24 GB)1x0.21
TensorDockA4000 (16 GB)1x0.13
TensorDockRTX 4090 (24 GB)1x0.37
TensorDockRTX 3090 (24 GB)1x0.22
TensorDockRTX 3080 Ti (12 GB)1x0.17
TensorDockRTX 3080 (10 GB)1x0.17
TensorDockRTX 3070 Ti (8 GB)1x0.14
TensorDockRTX 3060 Ti (8 GB)1x0.10
TensorDockRTX 3060 (12 GB)1x0.10
Vast.aiL40 (45 GB)1, 2, 4x1.10
Vast.aiA100 (40 GB)1, 2, 4x1.40
Vast.aiA40 (48 GB)1, 2x0.40
Vast.aiA6000 (24 GB)1, 2, 4, 8x0.44
Vast.aiA5000 (24 GB)1, 2, 4, 8x0.20
Vast.aiA4000 (16 GB)1, 2, 4, 5, 8x0.15
Vast.aiV100 (16 GB)2, 5x0.18
Voltage ParkH100 80 GB8x1.89****
VultrL40S 48 GB1, 2, 4, 8x1.75
VultrH100 80 GB1x7.50
VultrA100 80 GB1, 2, 4, 8x2.60
VultrA40 (48 GB)1, 4x1.83
VultrA16 (16 GB)1, 2, 4, 8, 16x0.51

*** On-demand price *($) per single GPU. Excludes cost of storage, network performance, ingress/egress etc. This is only the GPU cost.

**** Computed values. This was needed when single GPU instances were not available.5 6

Other cloud GPU considerations

Availability: Not all GPUs listed above may be available due to capacity constraints of the cloud providers and increasing demand for generative AI.

Data security: For example, cloud GPU marketplaces like Vast.ai offer significantly lower prices but depending on the specific resource requested, the data security of the workload could be impacted, givings hosts the capability to access workloads. Since we prioritized enterprise GPU needs, Vast.ai wasn’t included in this benchmark.

Ease of use: Documentation quality is a subjective metric but developers prefer some cloud providers’ documentation over others. In this discussion, GCP’s documentation was mentioned as lower quality than other tech giants’.7

Familiarity: Even though cloud providers put significant effort into making their services easy-to-use, there is a learning curve. That is why major cloud providers have certifications systems in place. Therefore, for small workloads, the cost savings of using a low cost provider may be less than the opportunity cost of the time it takes a developer to learn how to use their cloud GPU offering.

Buy GPUs or rent cloud GPUs

Buying makes sense

– If your company has the know-how and preference to host the servers or manage colocated servers.

– For uninterruptible workloads: For the volume of GPUs for which you can ensure a high utilization (e.g. more than 80%) for a year or more.8

– For interruptible workloads: The high utilization period quoted above needs to be a few times longer since on-demand (uninterruptible computing) prices tends to be a few times more expensive than spot (interruptible computing) prices.

Our recommendation for businesses with heavy GPU workloads is a mix of owned and rented GPUs where guaranteed demand runs on owned GPUs and variable demand runs on the cloud. This is why tech giants like Facebook are building their own GPU clusters including hundreds of GPUs.9

Buyers may be tempted to consider consumer GPUs which offer a better price/performance ratio however, the EULA of their software prohibits their use in data centers.10 Therefore, they are not a good fit for machine learning except for minor testing workloads on data scientists’ machines.

Cloud GPU benchmark methodology

Prices: Cloud GPU prices are crawled from

  • Monthly from the top 3 providers.
  • Twice a year from other providers.

Performance:

  • All GPU models performance were measured on AWS.
  • It is assumed that the same GPU provides the same performance in any cloud.
  • High-end models like H100 were not available and therefore are not included above.

Performance on:

  • Text finetuning was measured by finetuning Llama 3.2 with the first 5k conversations on FineTome using 1M tokens. Finetuning was carried over 5 epochs. Number of tokens times number of epochs was divided by finetuning time to identify number of tokens finetuned per second.
  • Text inference was measured during inference of 1 million tokens including both input and output tokens. We divided number of tokens by the total duration to calculate the average number of tokens per second during inference.
  • Image operations was measured by finetuning YOLOv9 with 100 images from SkyFusion for 4 epochs and then by inferencing the finetuned model with ~500 640×640 images.

Next steps:

  • Data collection frequency will be increased
  • We will run benchmarks in clouds other than AWS

What are the top cloud GPU hardware?

Almost all cloud GPUs use Nvidia GPU instances. AMD and other providers also offer GPUs however due to various reasons (e.g. limited developer adoption, lower price per performance etc.), their GPUs are not as widely demanded as Nvidia GPUs.

To see cloud GPU providers that offer non-Nvidia GPUs, please check out the comprehensive list of cloud GPU providers.

Read about all AI chips / hardware.

What are cloud GPU marketplaces?

Distributed cloud marketplaces like Salad, Vast.ai, and Clore.ai provide access to decentralized GPU computing power through a marketplace model. Users with idle hardware can list their GPUs for rent, while those needing GPU power can select from available resources at different price points. These platforms facilitate the connection between supply and demand without relying on centralized cloud providers. They offer a cost-effective and flexible solutions for GPU-intensive tasks.

Salad: decentralized network for tasks like AI training or crypto mining, with a focus on user rewards and ease of use.

Vast.ai: Connects GPU providers with users in need of affordable and scalable computational resources. Focus is on AI and machine learning workloads.

Clore.ai: A distributed marketplace for cloud GPUs. Focus is mostly on: AI, and other HPC needs.

Kryptex: A platform that enables users to earn cryptocurrency by renting out their GPUs. Main focus is to perform tasks like crypto mining or processing complex calculations.

Nvidia DGX Cloud Lepton: Connects AI developers with Nvidia’s network of cloud providers, including CoreWeave, Lambda, and Crusoe, to access GPU resources. Focus is on providing a flexible marketplace for training and deploying AI models across diverse cloud platforms.11

What are the top cloud GPU platforms?

Top cloud GPU providers are:

  • AWS
  • Microsoft Azure
  • CoreWeave
  • Google Cloud Platform (GCP)
  • IBM Cloud
  • Jarvis Labs
  • Lambda Labs
  • NVIDIA DGX Cloud
  • Oracle Cloud Infrastructure (OCI)
  • Paperspace CORE by DigitalOcean
  • Runpod.io

For more on these providers, check out cloud gpu providers.

If you are not sure about cloud GPUs, explore other options like serverless GPU.

For tools allow users to collaborate on, check out cloud collaboration tools.

If you are unclear about what cloud GPUs are, here is more context:

What is a cloud GPU?

Unlike a CPU, which may have a relatively small number of cores optimized for sequential serial processing, a GPU can have hundreds or even thousands of smaller cores designed for multi-threading and handling parallel processing workloads.

A cloud GPU refers to a certain way of GPU usage that’s provided as a service through cloud computing platforms. Much like traditional cloud services, a cloud gpu allows you to access high-performance computing resources spot or on-demand, without the need for upfront capital investment in hardware.

What are the functions/application areas of cloud GPUs?

Cloud GPUs are primarily used for processing tasks that require high computational power. Here are some of the primary uses for cloud GPUs:

Machine Learning and AI

GPUs are particularly effective at handling the complex calculations required for machine learning (ML) and artificial intelligence (AI) models. They can process multiple computations in parallel, making them suitable for training large neural networks and algorithms.

Deep learning

Deep learning is a sub-field of machine learning. Deep learning algorithms greatly benefit from the parallel processing capabilities of GPUs, making training and inference faster and more efficient.

Data processing

Data analysis

GPUs are used to accelerate computing and data processing tasks, such as Big Data analysis and real-time analytics. They can handle high-throughput, parallel processing tasks more efficiently than CPUs.

Scientific computing

In scientific research, cloud GPUs can handle computations for simulations, bioinformatics, quantum chemistry, weather modeling, and more.

Simulations

Certain complex simulations can run more efficiently on GPUs.

Gaming & entertainment

Cloud GPUs are used to provide cloud gaming services, such as Google’s Stadia or NVIDIA’s GeForce Now, where the game runs on a server in the cloud, and the rendered frames are streamed to the player’s device. This allows high-quality gaming without the need for a powerful local machine.

Graphics rendering

GPUs were initially designed to handle computer graphics, and they still excel in this area. Cloud GPUs are used for 3D modeling and rendering, 3D visualizations, virtual reality (VR), computer-aided design (CAD), and computer-generated imagery (CGI).

Video processing

They’re used in video encoding and decoding, video editing, color correction, effects rendering, and other video processing tasks.

Cryptocurrency mining

GPUs are also used in tasks like cryptocurrency mining. However application-specific integrated circuits (ASICs) are offering better economics for more commonly mined crypto currencies.

Notes

Cloud providers are constantly updating their offering, this research will be constantly updated.

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem 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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2 Comments
Ashley Jenkinson
Oct 31, 2024 at 08:54

Cem – great article, I’d love to pick your brain on private networking or direct connects to these GPU instances.

Cem Dilmegani
Nov 10, 2024 at 06:58

Hi Ashley, thank you! Sure, happy to chat.

Harsh Sharma
Oct 06, 2024 at 02:19

Hi there, fantastic article and very well-researched. Would you mind checking out Dataoorts at https://dataoorts.com

Cem Dilmegani
Oct 22, 2024 at 03:18

Sure, we’ll review to see if we can include Dataoorts in the next edit.

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