The Perfect Pair: FlokiNET Servers & GPUs

In the fast-evolving world of cloud computing and data processing, having the right infrastructure is essential to stay ahead. At FlokiNET, we understand that combining powerful servers with high-performance Graphics Processing Units (GPUs) can transform the way businesses approach AI, machine learning, gaming, and data analytics. In this article, we’ll explore the possibility of integrating FlokiNET Servers and High-Performance GPUs and how this combination can take your operations to new heights.

What is a GPU and Why Should You Care?

Before diving into the specifics of GPU-powered servers, let’s take a moment to explain what a Graphics Processing Unit (GPU) is and why it’s become indispensable in modern computing.

What Exactly is a GPU?

A GPU is a specialized hardware component designed for rendering graphics, processing images, and handling computations that require high parallel processing power. Initially used in gaming and graphics-intensive tasks, GPUs are now widely used for a variety of purposes, such as machine learning, data analytics, and AI training.

What is a GPUs with CUDA Support?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows developers to use the power of NVIDIA GPUs for general-purpose computing tasks, beyond just rendering graphics. Essentially, CUDA enables GPUs to perform highly parallel computations, making them ideal for complex tasks such as machine learning, deep learning, scientific simulations, and big data analysis.

How Does a GPU Differ from a CPU?

Unlike the Central Processing Unit (CPU), which is designed for serial processing (handling one task at a time), the GPU can process thousands of smaller tasks simultaneously. This makes GPUs extremely efficient for parallel processing tasks like deep learning, image rendering, and big data analytics.

• CPU: Optimized for general-purpose computing tasks, handling a few tasks sequentially.
• GPU: Specialized for parallel processing, excelling in tasks that require massive simultaneous computations.

Why Do We Need a GPU?

A GPU is essential for tasks that require heavy computational power, such as machine learning and data processing. By using GPUs, you can process large datasets significantly faster, allowing for quicker AI model training, real-time data analysis, and enhanced 3D rendering.

What is tensor precision?

Tensor precision refers to the numerical format or level of detail used to represent and store numbers in tensor operations, particularly when using Tensor Cores in NVIDIA GPUs. These cores are optimized for performing matrix operations, which are fundamental to many machine learning algorithms. Tensor precision is important because it affects both the speed and accuracy of computations.

What GPUs do we offer at FlokiNET?

At FlokiNET, we offer a range of GPU dedicated servers equipped with top-tier Nvidia GPUs to meet diverse computational needs.

1. NVIDIA A100:

• (TF32, BFLOAT16, FP16, INT8)
• Ideal for high-precision tasks such as scientific computing, engineering applications, physics simulations, deep learning training and inference. A versatile choice for data centers focused on AI, machine learning, and HPC workloads.
• A perfect match with the following dedicated servers: DL380 Gen 10 and DL385 Gen 10 Plus V2!

2. NVIDIA L40S:

• (FP32, TF32, BFLOAT16, FP16, FP8, INT8)
• The BEST universal GPU is built on the NVIDIA Ada Lovelace architecture and boasts exceptional performance for a wide array of applications: Generative AI, image generation (~200 stable diffusion images/minute), Large Language Model (LLM) Inference, 3D Graphics, Rendering, video processing.
• The L40S is data center-ready, offering reliability, security features like secure boot, and compliance with industry standards like NEBS Level 3.
• Excels in for deep learning, AI training and inference, edge computing and real-time video analytics.
• Another perfect match with our DL380 Gen 10, DL385 Gen 10 Plus V2!

3. NVIDIA L4 Tensor Core GPU:

• (FP32, TF32, BFLOAT16, FP16, FP8, INT8)
• Versatile accelerator designed to enhance a wide array of workloads, including video processing, AI tasks, virtual desktops, and graphics rendering. Leveraging the NVIDIA Ada Lovelace architecture and fourth-generation Tensor Cores, it delivers exceptional energy efficiency and up to 120X faster AI video performance compared to CPU-based solutions. Its single-slot, low-profile form factor makes it easy to integrate into various server environments.
• Efficient in for inference tasks, media processing, virtualized workloads, edge AI, real-time video processing, IoT applications, smart devices, and financial AI. Ideal for the following dedicated servers: DL360 Gen 10, DL360 Gen 11 and BL460c Gen 10!

4. NVIDIA A2:

• (FP32, TF32, BFLOAT16, FP16, INT8)
• Suitable for entry-level AI inference, edge computing, and virtualization tasks. Its low power consumption and compact design make it ideal for smart devices, real-time analytics, and mobile AI applications. It is an excellent choice for edge deployments and scenarios requiring efficient performance in constrained environments. Perfect match with DL360 Gen 10, DL360 Gen 11 and BL460c Gen 10!

5. NVIDIA T4:

• (FP32, FP16, INT8)
• Designed for a wide range of modern applications including machine learning, deep learning, and virtual desktops. Its energy-efficient design and small form factor make it ideal for mainstream enterprise servers, cloud computing, and edge deployments. The T4 is particularly effective for inference tasks, media processing, and virtual desktop infrastructure (VDI). Compact, energy-efficient powerhouse designed for diverse workloads.  Another perfect match with DL360 Gen 10, DL360 Gen 11 and BL460c Gen 10!

GPU Paring with FlokiNETs servers

Why Choose FlokiNET Dedicated Servers for GPU Integration?

FlokiNET is kown for its high-performance servers, designed to handle complex workloads. When paired with GPU-powered systems, these servers become a powerhouse capable of managing demanding applications like AI models, gaming servers, and video rendering. Here’s why FlokiNET servers are the perfect choice for integrating GPUs:

• High-Performance Hardware

Our servers are built to support GPUs, offering the necessary resources to ensure top-tier performance in areas such as machine learning, cloud gaming, and video streaming. FlokiNET servers are designed with scalability in mind, making it easy to add GPU instances as needed.

• Energy Efficiency

Using GPU-powered servers can help reduce energy consumption compared to traditional CPU servers, especially when performing tasks like deep learning. GPUs are optimized for parallel processing, meaning they complete tasks faster, allowing for more efficient use of resources. FlokiNET also uses green energy to power the servers, reducing the impact on the environment.

• Cost-Effectiveness

For businesses looking to scale their AI or machine learning operations, GPU servers can reduce the need for large server farms or clusters. This cost-efficiency makes it an attractive option for startups and established enterprises alike, particularly when hosted with FlokiNET’s reliable server infrastructure.

FlokiNETs dedicated servers with GPU

Now let’s match you up with our servers!

Let’s see how we can team up to help you make the best decision for your server. A more elaborated match series on how to choose the right GPU for your FlokiNET dedicated server can be found below. 

360 Gen 10 – 2x Platinum 8173M, 28-core CPUs

• Ideal for AI and Machine Learning: For this high-performance setup, the NVIDIA Tesla T4 would be an excellent choice. This server offers high computational power and is optimized for AI and machine learning workloads.

380 Gen 10 – 2x Gold 6132, 14-core CPUs (mostly for LFF storage and GPU)

• Great for Storage and GPU Tasks: The NVIDIA L4 could be a suitable option for this server. It provides a balanced performance for storage and GPU-intensive tasks, aligning well with the server’s configuration and intended usage.

385 Gen 10 Plus V2 – 2x AMD EPYC 7543, 32-core CPUs

• Perfect for High-Performance Computing: Given the robust CPU configuration, the NVIDIA L40s would be a fitting choice. The server offers high performance and is well-suited for high-performance computing tasks, aligning with the capabilities of the 32-core CPUs.

360 Gen 11 – Gold 6448H, 32-core CPUs

• Ideal for Versatile Applications: This server can benefit from GPUs like the NVIDIA Tesla T4 or the NVIDIA L40s. The Tesla T4 excels in AI and machine learning tasks, while the L40s offers high performance for general computing tasks, ensuring versatility based on workload requirements.

Key Benefits of GPUs in Servers: The Perfect Combination with FlokiNET

FlokiNET’s infrastructure, when combined with high-performance GPUs, unlocks several key benefits for businesses, including:

• Faster Data Processing and Analytics

GPUs excel in handling big data and real-time analytics. For businesses involved in data science, financial analysis, or healthcare, integrating GPUs into FlokiNET’s servers will allow you to process large datasets significantly faster, leading to quicker insights and better decision-making.

• Enhanced Machine Learning Capabilities

Training AI models requires massive computational power. With GPU acceleration, you can train neural networks faster, improving the efficiency of your AI workflows. FlokiNET’s GPU-powered servers are optimized to handle these resource-intensive tasks, helping you stay ahead in the world of AI development.

• Optimized for Cloud Gaming and Video Rendering

For gaming companies and media production studios, GPU-powered servers can drastically reduce rendering times and improve the quality of graphics in real time. Flokinet’s dedicated servers in combination with GPU offer scalable solutions to meet the demands of gaming servers or high-quality video rendering workflows.

What Are the Best Use Cases for GPU-Powered Servers?

FlokiNET’s GPU-powered servers are versatile and cater to a wide range of applications. Here are some of the best use cases:

• Machine Learning & AI Training

Training deep learning models requires massive computational power. FlokiNET servers with GPUs are ideal for processing large datasets and training neural networks in a fraction of the time it would take with traditional CPU servers.

• Cloud Gaming

With the rise of cloud gaming, GPUs are essential for rendering high-quality graphics and delivering smooth, lag-free gameplay experiences to players worldwide. Our cloud-based GPU infrastructure is optimized to handle these demanding gaming applications, offering the best option for you.

• Big Data Analytics

When handling large datasets, speed and efficiency are paramount. GPU-powered servers accelerate data processing, allowing businesses to derive insights faster and make real-time decisions.

• Video Rendering & Media Production

For media production studios and designers, GPU acceleration can drastically cut down video rendering times while maintaining high-quality output. FlokiNET’s GPU-powered servers are perfect for these high-performance computing tasks.

Power Up Your Business with FlokiNET and GPUs

Integrating GPU with FlokiNET’s dedicated servers gives you the tools needed to stay ahead in an increasingly data-driven world. FlokiNET offers high-performance GPU server hosting options that come with GPUs optimized for CUDA (Compute Unified Device Architecture).

Whether you’re running machine learning models, processing big data, or powering cloud gaming services, FlokiNET’s GPU solutions offer the performance, and flexibility your business needs.

Frequently Asked Questions (FAQs)

• What types of GPUs you can order with FlokiNET’s dedicated servers?

FlokiNET offers a range of high-performance GPUs, including those designed for AI and big data analytics, to ensure optimal performance for your specific needs. We offer NVIDIA A100, NVIDIA L40S, NVIDIA L4 Tensor Core GPU, NVIDIA A2, NVIDIA T4.

• Is GPU hosting cost-effective for my business?

Yes, GPU in combination with Dedicated Servers offers clients who need constant load a cost-effective solution in comparison with cloud solutions.

• Is Using GPUs Environmentally Friendly?

Using GPUs (Graphics Processing Units) can be both environmentally friendly and energy-intensive, depending on how they are used. While GPUs typically consume more power than CPUs, they are more energy-efficient for tasks like machine learning, AI, and big data processing. In data centers, where large-scale parallel computing is needed, GPU-based systems can outperform CPU-based systems by completing tasks faster, thus reducing overall energy consumption.

At FlokiNET, we power our servers with green energy in the locations in Iceland, Finland and Netherlands, using renewable sources like geotermal, solar, wind, and hydroelectric power, ensuring our GPU hosting solutions are both efficient and environmentally responsible.

Have any questions or suggestions? Let us know on our social media platforms via ticket or send us an e-mail at info@flokinet.is and we’ll get right back to you!