Cloud GPU

A GPU, or Graphics Processing Unit, is a specialized processor designed for parallel computing tasks, such as rendering graphics or performing machine learning calculations. It differs from a CPU, or Central Processing Unit, which is a general-purpose processor that handles a variety of tasks.

Pricing for GPU instances in the public cloud varies depending on the provider, instance type, and region. Generally, GPU instances cost more than standard CPU instances due to the specialized hardware and increased performance.

GPUs can significantly accelerate certain types of computing workloads, such as machine learning, scientific simulations, and video rendering. They can perform complex calculations in parallel, which can speed up processing time and reduce costs compared to traditional CPU-based computing.

We guarantee monthly availability for GPU instances with SLA at 99.999 %.

Our cloud GPUs with powerful hardware acceleration handles parallel processing for deep learning, complex processing workloads and are accelerated by the NVIDIA, harnessing the power of CUDA, Tensor, and RT cores.

GPU instances are invoiced on a pay-as-you-go basis at the end of each month, just like all other ACH public cloud instances. The cost is determined by the instance size you’ve booted and the time period for which you’ll be using it.

Applications that require complex mathematical calculations, large-scale data processing, or high-speed image rendering can benefit from GPU-accelerated computing. Examples include machine learning, video rendering, and scientific simulations.

The right GPU instance type depends on your workload requirements, such as the amount of memory, storage, and processing power needed. You should also consider factors such as cost, network bandwidth, and regional availability when selecting an instance type.

Yes, most public cloud providers allow you to scale up or down GPU instances as needed, based on workload demands.

As with any computing resource, there are security considerations when using GPU instances in the public cloud. You should ensure that your applications are properly secured and that access to GPU instances is restricted to authorized users.

Configuration and management of GPU instances in the public cloud depend on the provider and instance type. Generally, you can use management tools provided by the cloud provider to create, deploy, and monitor GPU instances.

Some limitations to using GPU instances in the public cloud include regional availability, cost, and compatibility with specific applications or software frameworks.

Technical support for GPU instances in the public cloud is typically provided by the cloud provider. This may include online documentation, user forums, and support from technical specialists.

ACE’s pricing for NVIDIA A100 may vary based on location and storage requirements. Contact ACE for a personalized quote.

Tensor Cores are specialized cores that enable multi-precision computing for efficient AI inference. They dynamically adjust algorithms to improve throughput while maintaining accuracy.

You can request a quota increase for NVIDIA A100 GPU by submitting a ticket on ACE’s website, which will be approved within 24 hours.

You can choose the GPU from NVIDIA A2, NVIDIA A30, and NVIDIA A100 with additional configurations, features, and pricing based on your workload requirements.

To claim $300 free credit, sign up on ACE’s website with your email and mobile number, and complete payment verification by paying $1. After successful verification, you will receive $301 credits to use ACE’s services.

Any client who completes the $1 payment and KYC process is eligible for $300 + $1 credits.

Yes, any data on instance store volumes is lost if the instance is stopped or terminated. Data on an instance store volume only persists for the duration of the associated instance.

GPUs can handle multiple computations simultaneously, speeding up machine learning processes. They allow adding more cores without compromising performance or power.

ACE offers round-the-clock support, and their technical team promptly investigates and responds to all customer queries.

NVIDIA A100, NVIDIA A30, and NVIDIA A2 are among the best GPUs for deep learning and heterogenous AI workloads. These GPUs offer the power needed for AI development and deployment at scale.