Your company's GPU computing strategy is essential whether you engage in 3D visualization, machine learning, AI, or any other form of intensive computing.
There was a time when businesses had to wait for long periods of time while deep learning models were being trained and processed. Because it was time-consuming, costly, and created space and organization problems, it reduced their output.
This problem has been resolved in the most recent GPU designs. Because of their high parallel processing efficiency, they are well-suited for handling large calculations and speeding up the training of your AI models.
When it comes to deep learning, Cloud GPUs can speed up the training of neural networks by a factor of 250 compared to CPUs, and the latest generation of cloud GPUs is reshaping data science and other emerging technologies by delivering even greater performance at a lower cost and with the added benefits of easy scalability and rapid deployment.
This article will provide an overview of cloud GPUs, their applications in artificial intelligence, machine learning, and deep learning, and the top cloud GPU deployment platforms available today.
Liquid Web, a prominent provider of managed hosting and cloud solutions, has recently introduced its GPU hosting services to meet the escalating demands of high-performance computing (HPC) applications. This offering is tailored for tasks such as artificial intelligence (AI), machine learning (ML), and rendering workloads, providing businesses with the computational power necessary to handle data-intensive operations efficiently.
Liquid Web's Cloud GPU Hosting Services are designed to deliver exceptional performance for resource-intensive applications. By integrating NVIDIA's advanced GPUs, including models like the L4 Ada 24GB, L40S Ada 48GB, and H100 NVL 94GB, these services cater to a wide range of computational needs. Each server configuration is optimized to ensure seamless operation for AI/ML tasks, large-scale data processing, and complex rendering projects.
-
High-Performance Hardware: The servers are equipped with powerful NVIDIA GPUs and AMD EPYC CPUs, ensuring robust processing capabilities. For instance, the NVIDIA L4 Ada 24GB model comes with dual AMD EPYC 9124 CPUs, offering 32 cores and 64 threads at 3.0 GHz (Turbo 3.7 GHz), 128 GB DDR5 memory, and 1.92 TB NVMe RAID-1 storage.
-
Optimized Software Stack: The GPU stack includes the latest NVIDIA drivers, CUDA Toolkit, cuDNN for deep learning, and Docker with NVIDIA Container Toolkit, facilitating efficient deployment and management of AI/ML workloads.
-
Scalability: Liquid Web offers a range of server configurations to meet varying performance requirements, allowing businesses to scale resources as their computational needs evolve.
-
Compliance and Security: The hosting services adhere to strict compliance standards, including PCI and SOC compliance, and undergo HIPAA audits, ensuring the security and integrity of sensitive data.
Liquid Web provides several GPU server configurations with corresponding pricing:
-
NVIDIA L4 Ada 24GB: Priced at $880 per month, this configuration includes dual AMD EPYC 9124 CPUs, 128 GB DDR5 memory, and 1.92 TB NVMe RAID-1 storage.
-
NVIDIA L40S Ada 48GB: Available for $1,580 per month, it features dual AMD EPYC 9124 CPUs, 256 GB DDR5 memory, and 3.84 TB NVMe RAID-1 storage.
-
NVIDIA H100 NVL 94GB: This premium option is offered at $3,780 per month, comprising dual AMD EPYC 9254 CPUs, 256 GB DDR5 memory, and 3.84 TB NVMe RAID-1 storage.
-
Dual NVIDIA H100 NVL 94GB: For intensive computational needs, this configuration is priced at $6,460 per month and includes dual AMD EPYC 9254 CPUs, 768 GB DDR5 memory, and 7.68 TB NVMe RAID-1 storage.
Due to high demand, delivery times for GPU servers range from 24 hours to two weeks.
Pros:
- High Performance: Utilization of advanced NVIDIA GPUs ensures exceptional processing speeds suitable for AI/ML and rendering tasks.
- Comprehensive Software Stack: Pre-configured with essential tools and frameworks, facilitating efficient deployment of AI/ML workloads.
- Scalability: Flexible configurations allow businesses to adjust resources based on their evolving needs.
- Compliance: Adherence to industry standards ensures data security and regulatory compliance.
Cons:
- Cost: The premium hardware and services come at a higher price point, which may be a consideration for smaller businesses.
- Availability: High demand may lead to longer delivery times for certain configurations.
- AI and Machine Learning: Accelerating training and inference of deep learning models, deploying real-time AI services, and hosting pre-trained large language models.
- Data Analytics: Speeding up big data processing and real-time analytics using GPU-optimized frameworks.
- Content Creation: Handling large-scale rendering and video editing tasks efficiently.
- Healthcare and Medical Imaging: Enhancing diagnostics, image analysis, and simulations requiring high computational power.
- High-Performance Computing: Supporting scientific research, climate modeling, genomics, and complex engineering simulations.
Liquid Web's GPU hosting services offer a robust solution for businesses seeking high-performance computing capabilities. With advanced hardware configurations, a comprehensive software stack, and adherence to compliance standards, these services are well-suited for a variety of data-intensive applications.
While the cost may be a consideration for some, the performance and scalability provided make it a compelling option for organizations aiming to leverage GPU-accelerated computing.
- Liquid Web Cloud GPU
- Latitude.sh
- OVHCloud
- Paperspace
- Vultr
- Vast AI
Let's start with GPUs to get a better grasp on cloud GPUs.
Graphics processing units (GPUs) are specialized electronic circuitry that can rapidly alter and manipulate memory to expedite the generation of images and graphics.
Modern graphics processing units are more effective at image and computer graphics manipulation than conventional central processing units (CPUs) due to their parallel structure (CPUs). The central processing unit (CPU) die, the PC's video card, or the motherboard could all house a GPU.
Massive artificial intelligence (AI) and deep learning tasks can be executed in the cloud using cloud graphics processing units (GPUs). In order to use this function, a GPU is not required.
Popular GPU manufacturers include AMD, NVIDIA, Radeon, and GeForce.
Have an idea and want to serve to world 🌎 , create a Webapp and deploy it as a flask , Django etc
| Vendor | Website | Pricing | Free Trial / Free Credits |
|---|---|---|---|
| Deta | https://www.deta.sh/ | pricing 🏷️ | Free plan available |
| Digital Ocean | https://www.digitalocean.com | Pay as you go | Free $100 credits with github student pack |
| Glitch | https://glitch.com | - | - |
| Heroku | https://www.heroku.com | pricing 🏷️ | Free plan (model<500MB) |
| PythonAnywhere | https://www.pythonanywhere.com/ | pricing 🏷️ | Free Beginner Account Available |
| Render | https://render.com | pricing 🏷️ | - |
| Streamlit For Teams | https://www.streamlit.io/ | pricing 🏷️ | Currently in Beta ( Streamlit Cloud Tool ) |
| Zeit | https://zeit.co | pricing 🏷️ | Free plan available |
A Beautiful marriage 💍 between Machine Learning and DevOps ( A Match Made in Heaven )
Working on Serious Enterprise Level projects that has potential to serve millions of people and make 💰 , leave it to the power ⚡ of DevOps to manage your Machine Learning LifeCycle
| Project / Platform | Website | Pricing | Free Trial / Free Credits |
|---|---|---|---|
| Akira.ai | https://www.akira.ai/mlops-platform/ | pricing 🏷️ | - |
| Algo | https://www.algomox.com/aiops | - | Free Edition Available |
| Algorithmia | https://algorithmia.com/ | pricing 🏷️ | - |
| Allegro | https://www.allegro.ai/ | pricing 🏷️ - for enterprise | Open Source & Enterprise Version |
| Amazon Sagemaker | https://aws.amazon.com/sagemaker/ | pricing 🏷️ | Available for free as part of AWS Free Tier |
| Arrikto | https://arrikto.com/ | - | - |
| ClearML | https://clear.ml | pricing 🏷️ | Free plan available |
| Cnvrg | https://cnvrg.io/platform/mlops/ | pricing 🏷️ | - |
| DataRobot | https://www.datarobot.com/platform/mlops/ | - | $500 of free usage credits across products |
| Flyte | https://flyte.org/ | - | Open Source  Link |
| Google Cloud AI Platform | https://cloud.google.com/ai-platform/ | pricing 🏷️ | - |
| Gradient from Paperspace | https://gradient.paperspace.com/ | pricing 🏷️ | Free GPUs by Gradient |
| Grid.ai | https://grid.ai/ | pricing 🏷️ | $25 free credits + special promo for researchers! |
| HPE - Ezmeral | Solution from HP | - | |
| HPE - GreenLake | Solution from HP | - | |
| Iguazio | https://iguazio.com/mlops/ | - | 14 Day Free Trial |
| KubeFlow ( for k8s ) | https://www.kubeflow.org/ | - | Open Source Link |
| MLFlow | https://mlflow.org/ | - | Open Source |
| Neptune.ai | https://neptune.ai/ | pricing 🏷️ | Freemium |
| Neu.ro | https://neu.ro/ | - | - |
| Seldon Core | https://seldon.io/tech/products/core/ | - | - |
| Valohai | https://valohai.com | pricing 🏷️ | - |
If you are a student or researcher you can get extra credts , contact the provider
-
Examesh supports Public Research for free and gives special discount to long-term bookings.
-
Paperspace provides $10 of free Gradient° credit fast.ai link
-
Do you have a GPU lying around rent your machine to Earn money using Vast.ai*
-
Test Drive Nvidia GPU link
-
AWS Cloud Credits for Research -link
-
Nvidia GPU Grant Program- link
-
If you are a Startup then google has you covered wth Startup Program giving you credits from $1000 to $100000 - link
-
Google giving cluster of 1000 TPUs to researcher In total, this cluster delivers a total of more than 180 petaflops of raw compute power! techcrunch link - application link
-
Google cloud Education Grant - link
-
Github Education pack - along with many offers has upto $110 credits for AWS - link
-
Watch out on fast.ai Forums to get coupon code for free credits
-
Want to use a Super Computer but don't have one, go for Golem - Golem is a decentralized marketplace for computing power. It enables CPUs and GPUs to connect in a peer-to-peer network, enabling both application owners and individual users to rent resources from other users machines, so turbo charge your next model training.
-
Hostkey provides grants for research, startups and competition winners link
- Google colab and Kaggle kernels have limited session time
- Most of the gpu providers run on top of AWS , GCP etc so may have more or less same pricing as the latter
- Information given above is best to my searching ability , you may recheck with the provider for pricing and other info
Recommended reading: