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Power AI with Hyperstack and NVIDIA GPUs

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cloud gpu for ai

Benefits of Cloud GPU for AI

Nothing beats NVIDIA GPU performance on Hyperstack for artificial intelligence applications.

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Master deep learning and neural networks

Accelerate the AI training and scalable inference of complex neural networks, unlocking the potential for highly accurate and advanced artificial intelligence models.

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Elevate your AI training

Achieve swift training, faster experimentation, and rapid AI model enhancement.

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Experience real-time inference

Unleash the power of Hyperstack's GPU cloud for lightning-fast real-time AI inference.

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Handle massive amounts of data

Accelerate data processing crucial for natural language processing, image recognition, and genomics research tasks.

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AI Ready hardware

NVIDIA GPUs are designed for AI acceleration and offer unprecedented performance for AI workloads.

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AI Solutions

Hyperstack grants access to NVIDIA's advanced AI tech for speeding up your work.

Generative AI

Our environments can be configured to suit any kind of AI workload, and can scale inferencing capabilities as your demand needs it. Leverage NVIDIA frameworks like NeMo to advance and simplify the development of conversational AI systems, including automatic speech recognition (ASR) and natural language processing.

AI Training

Reveal the full potential of LMS and Gen AI with Cloud-Based Training. Unlock scalable, multi-node training capabilities that guarantee cost-effective performance. Seamlessly integrate top-tier AI supercomputing with lightning-fast, low-latency network fabrics and developer-friendly tools, streamlining your workflow.

Data Analytics

With Hyperstack you can iterate swiftly on extensive datasets, increase model deployments, and reduce overall ownership costs.
High-performance data analytics empowers businesses to enhance customer service, accelerate product development, and drive innovation.
Leveraging 16 NVIDIA A100s equals the output of 350 CPU-based servers, delivering HPC-level performance with a remarkable 7x cost efficiency through Hyperstack's solution.

Use Cases

Artificial intelligence is used in healthcare, trading, business, and many other fields, transforming industries and improving efficiency.

ai applications in healthcare
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AI Applications In Healthcare

  • Drug discovery
  • Diagnosis and treatment
  • Health monitoring
ai applications in finance
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AI Applications In Finance

  • Fraud Detection
  • Virtual Reality & Augmented Reality
  • Health monitoring
manufacturing
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AI Applications In Manufacturing

  • Predictive maintenance
  • Quality control
  • Supply chain optimisation
Education
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AI Applications In Education

  • Personalised learning
  • Automated grading
  • Tutoring
ai applications in healthcare
ai applications in finance
manufacturing
Education

GPUs we Recommend for AI

Elevate your AI solutions and applications with Hyperstack's cutting-edge GPUs.

A100

A100

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Supercharge inference with H100s: achieve up to 30X acceleration and ultimately experience low latency.

h100 PCIe

H100 PCIe

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Unlock the potential of A100s for AI model training, advanced model analysis, and accurate predictions.

h100 sxm

H100 SXM

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Unlock the potential of A100s for AI model training, advanced model analysis, and accurate predictions.

AI Applications

We're dedicated to delivering top-notch GPU solutions that fuel innovation and ensure a swift and adaptable experience.

warehouse scenario

#1 Warehouse Scenario

The goal was to simulate a conveyor belt with all physical properties to test its capacities. Speed and other properties that can affect the powers of a conveyor belt have been tested. Some particular vehicle extensions were used to simulate vehicle movement in a warehouse. 

riva voice recognition scenario

#2 Riva voice recognition scenario

Riva converted text to speech and vice versa so that you could speak with the AI assistant in the application setup. The team worked with the Lip-Sync functionality, where the animated 3D avatar reproduced the text said into the microphone. Additionally, subtitles have been included, transcribing the spoken content from the microphone.

talkio ai scenario

#3 Talkio AI scenario

Within the Talkio AI application, you can train the models to respond to questions that can be spoken into the microphone, utilising the extensive capabilities of the Large Language Model (LLM). So, with this app, you can enhance your language skills, master some dialects, get ready for a language test, or even make a booking or an order in the restaurant.

Frequently Asked Questions

We build our services around you. Our product support and product developmentgo hand in hand to deliver you the best solutions available.

Can I rent my GPU for AI?

Yes, you can. GPUs excel at running Machine Learning and AI-based workloads due to their ability to handle large datasets in parallel.

Is GPU or CPU better for AI?

Both GPUs and CPUs play their role in AI but for complex tasks involving large datasets and parallel processing, GPUs excel due to their specialised architecture.

How to use cloud GPU for AI?

To use a cloud GPU for AI on HyperStack, you need to sign up or login to the platform and then:

1. Create your first environment

The first step is to create an environment. Every resource such as keypairs, virtual machines, volumes live in an environment. 

To create an environment, simply input the name of your environment and select the region in which you want to create your environment.

2. Import your first keypair

The next step is to import a public key that you'll use access your virtual machine via SSH. You'll need to generate an SSH key on your system first.

Then to import a keypair, simply select an environment in which you want to store the key pair in, enter a memorable name for your keypair, and enter the public key of your SSH keypair.

3. Create your virtual machine

We're finally here. Now that you've created your environment and keypair, we can proceed to create an virtual machine.

To create your first virtual machine, select the environment where you want to create your virtual machine in, select a flavor which is nothing but the specs of your virtual machine, select the OS image of your choice, enter a memorable name for your virtual machine, select the SSH key you want to use to access your virtual machine and then hit the "Deploy" button. Voila, your virtual machine is created.

To learn more please visit Hyperstack’s Documentation.

How much GPU is needed for AI?

The amount of GPU power needed for AI depends heavily on your specific project and the AI workload - a training environment will typically look very different to an environment for inference. Factors like dataset size, model complexity and desired training time all play a role. Start with a moderate configuration and scale up as needed.

See What Our Customers Think...

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