Nvidia V100S Maximizes AI and Machine Learning Performance at a Fraction of the Price

Machine learning is the digital world's new frontier, with tech-enabled AI companies developing increasingly advanced large language models that enable everything from image creation to crunching large datasets.

While the future of technology leans heavily towards AI, and the innovation surrounding it is largely fueled by Nvidia's Tensor core GPUs, there's a compelling argument to be made for the value and viability of the older V100S GPUs, which can still deliver massive parallel processing performance.

Amid the backdrop of newer models like A100 and H100 making waves in the market, the V100S, especially when interconnected, emerges as a highly cost-effective solution for AI applications.

By leveraging public cloud access to servers powered by 1, 2, or 4 V100S GPUs, businesses can achieve high computational bandwidth at a much-reduced cost. Moreover, as the demand for AI processing grows, the capability to scale up by interconnecting multiple V100S instances with OVHcloud’s proprietary vRack technology better ensures consistent and efficient performance.

V100S brings, particularly when it comes to crunching large datasets.

What Is a Nvidia Tesla V100S GPU?

NVIDIA's Tesla V100S (often just referred to as V100) is a high-end GPU designed specifically for high-performance computing (HPC), artificial intelligence (AI), and deep learning workloads. This GPU is based on the Volta architecture — hence the "V" in V100 — which is the successor to the Pascal architecture (e.g., P100).

Key Specs

• 5,120 CUDA Cores: These are the parallel processing units of the GPU, crucial for tasks like deep learning where many operations are performed simultaneously.
• 640 Tensor Cores: Introduced with the Volta architecture, Tensor Cores are designed specifically for deep learning calculations, providing immense speedups for certain matrix operations common in neural network training.
• 32 GB HBM2 (High Bandwidth Memory): HBM2 allows for much faster data access compared to traditional GDDR memory, making it useful for large datasets and models.
• 1,134 GB/s Memory Bandwidth: This is crucial for AI and HPC tasks where large amounts of data must be moved quickly.
• Performance: Up to 16.4 teraFLOPS of single-precision performance and 130 teraflops for deep learning with Tensor Cores. A teraFLOP measures computer performance through the amount of floating point calculations that can be done. One teraFLOP is, according to the Oxford Dictionary, "a unit of computing speed equal to one million million (1012) floating-point operations per second."

Nvidia V100S Uses

The V100 excels in deep learning training and inference tasks due to its Tensor cores, which are Nvidia's proprietary AI-specific computing cores. Its high memory bandwidth and capacity are also suitable for large models and datasets, making it a choice GPU for researchers and professionals in AI.

The V100 is used in supercomputers and HPC clusters worldwide. Its vast array of CUDA cores and high memory bandwidth makes it suitable for simulations, modeling, and other computational-heavy tasks that require vast amounts of parallel processing power.

Although it's primarily designed for computation, the V100 is still a graphical processing unit, and it can also handle large-scale visualization tasks, such as rendering and real-time simulation. Real-time physics simulations are used by scientists to simulate weather phenomena such as tsunamis. Computer simulations are what allowed the film Interstellar to accurately simulate what a black hole would like years before the first photo of a black hole was taken.

Use Cases of Cloud GPU V100S are as follows:

• Cloud-based Analytics and Insights: Leveraging deep learning and advanced mathematical simulations for data-driven decision-making.
• Cloud Render Farms: Utilizing GPU-accelerated rendering for CGI, animation, and VFX production.
• Virtual Desktop Infrastructure (VDI): Providing graphics-intensive virtual applications and desktops for remote users.
• Cloud-hosted CAD Applications: Offering high-fidelity video encoding, rendering, and streaming services on demand.
• Image Analysis and Processing: Using cloud infrastructure for large-scale image recognition and analysis is beneficial for sectors like medical imaging and geospatial services.
• 3D Graphics Simulations: Running intensive computational 3D simulations for scientific research, urban planning, and more.
• Video as a Service (VaaS): Streamlining business communication with cloud-based video encoding, decoding, and high-quality streaming.
• Cloud-driven Graphic Design: Allowing designers to access high-end graphic design applications from anywhere, facilitating collaboration and scalability.
• Content Delivery Networks (CDN): Utilizing GPU to enhance media streaming services, better ensuring efficient content delivery to global audiences.
• Financial Modeling and Simulations: Carrying out complex financial models and simulations in the cloud for real-time risk analysis, trading strategies, and more.
• AI and Machine Learning Training: Leveraging the computational power of GPUs in the cloud to train large-scale machine learning models efficiently.

OVHcloud US V100S T2-LE Instances Yield More Performance at Lower Costs

Cloud data centers like OVHcloud can leverage older GPUs by creating GPU-to-GPU connections that provide multitudes more bandwidth than a single A100 GPU. When hooked up, each V100S GPU has 32 GB/s interconnect bandwidth. OVHcloud offers cloud GPU servers that provide either one, two, or four V100S GPUs. The instances with two and four GPUs have their memory and cores multiplied and are available in a single system for applications that can handle scalable workloads running across multiple GPUs. Our V100S-powered server instances are available in our data centers at Vint Hill, Virginia, and Hillsboro, Oregon ensuring ping between your device and the server remains low.

OVHcloud Cloud GPU Computing

With OVHcloud's V100S-powered cloud GPU servers, anyone with a need to access HPC resources can do so affordably. Our T2-LE servers, which can use up to four V100S GPUs, offer the right balance of performance and price, allowing everyone, from startup founders to enterprise companies, to tap into machine learning resources without having to build their own infrastructure. Users who need additional cloud GPU resources can step away from the public cloud and into the private cloud, where we can link up multiple cloud GPU servers with OVHcloud proprietary vRack technology to offer you the computing bandwidth you need. Having two US-based data center options in Vint Hill and Hillsboro enables customers to access resources that are geographically closer to their location.

If you're ready to ramp up your AI project, or you're looking to start a new one, learn more about our cloud GPU services, or contact one of our sales representatives.