The Perfect Pair: FlokiNET Servers and High-Performance GPUs

Servers with High-Performance GPUs.

FlokiNET offers a range of dedicated servers that can be paired with powerful GPUs to meet your demanding workloads. Lets have a look at what our GPU portfolio has to offer!


– NVIDIA A100: Ideal for high-precision tasks such as scientific computing, engineering applications, and physics simulations. Its extensive support for various tensor precisions (TF32, BFLOAT16, FP16, INT8) makes it highly suitable for deep learning training and inference, making it 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! 

-NVIDIA L40S: the BEST universal GPU is built on the NVIDIA Ada Lovelace architecture and boasts exceptional performance for a wide array of applications, including:
Generative AI: Supercharge image generation with up to 200 stable diffusion images per minute.
Large Language Model (LLM) Inference: Experience lightning-fast inference with low latency for models like Llama 2.
3D Graphics and Rendering: Accelerate complex workflows with third-generation RT Cores and hardware-accelerated motion blur.

Video Content: Streamline video processing and enhance AI video performance with up to 99% better energy efficiency.
The L40S is data center-ready, offering reliability, security features like secure boot, and compliance with industry standards like NEBS Level 3.

Excels in FP32, TF32, BFLOAT16, FP16, FP8, INT8 for deep learning, AI training and inference, graphics rendering, edge computing, real-time video analytics, healthcare AI, autonomous vehicles, and recommendation systems. Another perfect match with our DL380 Gen 10, DL385 Gen 10 Plus V2! 


-NVIDIA L4 Tensor Core GPU: 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 FP32, TF32, BFLOAT16, FP16, FP8, INT8 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! 

– NVIDIA A2: 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.

Suitable for FP32, TF32, BFLOAT16, FP16, INT8 for entry-level AI inference, edge computing, virtualization, smart devices, real-time analytics, autonomous systems, healthcare AI, and mobile AI applications. Perfect match with DL360 Gen 10, DL360 Gen 11 and BL460c Gen 10! 

– NVIDIA T4: 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. Powered by NVIDIA Turing Tensor Cores, it excels at accelerating deep learning, machine learning, video trans-coding, and virtual desktops. Its low-profile, single-slot design makes it ideal for space-constrained environments, while its 70W power limit ensures optimal efficiency. Another perfect match with DL360 Gen 10, DL360 Gen 11 and BL460c Gen 10! 


GPU and the AI Development

GPU and the AI Development.

GPUs have revolutionized the field of AI development, enabling researchers and developers to train complex models faster and more efficiently. Their parallel processing capabilities are perfectly suited for the computationally intensive tasks involved in AI, such as deep learning and neural networks.

Choosing the right GPU for your server is a crucial step in optimizing your infrastructure for the tasks at hand. By carefully considering your needs, budget, future plans, and the technical specifications of each option you can make an informed decision that will empower your server to reach its full potential.

Feel free to reach out to FlokiNET’s experts for personalized guidance and support in selecting the ideal GPU for your specific requirements.

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 – Start at €​​​​​​​467.00/m!

  • Ideal for AI and Machine Learning: For this high-performance setup, the NVIDIA Tesla T4 would be an excellent choice. It 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) – Start at €433.00/m!

  • 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 – Start at €5978.90/half year!

  • Perfect for High-Performance Computing: Given the robust CPU configuration, the NVIDIA L40s would be a fitting choice. It 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 – Start at €7953.00/half-year!

  • 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.

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

7 Tips to Help You Choose the Right GPU

How to Choose the Right GPU.

How to Choose the Right GPU for Your Server: Ultimate Guide

Selecting the right GPU for your server is a critical decision, as it directly impacts performance, efficiency, and ultimately, your bottom line. Whether you’re into the AI, rendering high-definition videos, or running complex simulations, your GPU is the engine that powers your server’s capabilities. This guide will walk you through the essential factors to consider and offer 7 expert tips to ensure you make the best choice. But first things first.

What is a GPU?

What is a GPU?

In a nutshell it is a specialized processor designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. While originally developed for rendering graphics, GPUs have evolved to excel in parallel processing, making them ideal for tasks that require handling massive amounts of data simultaneously.

While both GPUs and CPUs are essential components of a computer system, they serve distinct purposes.

  • CPU: Responsible for executing sequential instructions and general-purpose tasks. It is able to handle a wide variety of operations but is less efficient at processing large datasets in parallel.
  • GPU: Designed for parallel processing, GPUs excel at handling multiple tasks simultaneously. This makes them perfect for tasks like rendering graphics, running simulations, and accelerating machine learning algorithms.

What are the different features that GPUs for servers have?

Server GPUs are specifically designed for data center environments and are optimized for performance, efficiency, and reliability. Below we will be comparing a few cards from our product list, based on their features:

  • Higher memory bandwidth: To handle large datasets efficiently.
    •  A100: 1,935GB/s
    •  L40S: 864GB/s
    •  L4: 300GB/s
    •  A2: 200GB/s
  • Precision: refers to the detail and accuracy with which numerical values are represented and processed, influencing the performance and suitability of the GPU for various computational tasks:
    • FP64 (High Precision): Used in scientific computing for simulations, climate modeling, engineering applications, financial modeling, physics simulations.
    • FP32 (Medium Precision): Utilized in graphics rendering for game development, 3D modeling, and animation, general-purpose computing, machine learning training, data analytics, VR, real-time simulations, and computational photography.
    • Tensor Float 32 / BFLOAT16 / FP16 (Low Precision): Ideal for deep learning with large datasets, AI research, image recognition, natural language processing, recommendation systems, speech recognition, autonomous vehicle development and scalable AI services in cloud computing.

When choosing the right GPU for your dedicated server keep these tips in mind: 

7 tips to choose the right GPU for your server.

1. Identify Your Needs

The first step is understanding the exact role your GPU will play in your server environment. Are you building a powerhouse for AI and machine learning algorithms? Do you need to render high-quality videos or graphics? Perhaps you’re hosting a demanding gaming server. Each use case has different demands on GPU capabilities.

2. Determine Your Budget
GPUs come in a wide range of prices. It’s essential to establish a realistic budget that aligns with your needs and overall project goals. Don’t forget to factor in additional costs like power consumption and cooling requirements, as high-performance GPUs can be power-hungry. Balancing performance with cost is key to getting the most value out of your investment.

3. Evaluate Your Current Infrastructure
Before diving into GPU selection, take a close look at your existing server setup. What resources do you rely on currently? Go through the documentation of the GPU you plan on using and compare its features and compatibility with the current set-up you have and how you plan to scale/grow.

Consider the following:

  • PCIe Slots: Ensure your server motherboard has the necessary PCIe slots to support the GPU.
  • Power Supply: Check if your power supply unit (PSU) can provide sufficient power for the GPU.
  • Physical Space: Verify that your server chassis has enough space to accommodate the GPU, especially if it’s a large, high-end model.
  • Driver Support: Ensure that the GPU has stable drivers that are compatible with your server’s operating system and software stack.

4. Explore Various GPU Server Choices
While the market is flooded with multiple options – we have a select list of GPUs, each offering unique features and specifications. Take the time to research different models and compare:

  • CUDA Cores/Stream Processors: These determine the GPU’s ability to handle parallel tasks. More cores generally mean better performance for complex calculations.
  • VRAM (Video RAM): This is essential for handling large datasets and textures. Ensure the GPU has enough VRAM to manage your applications without bottlenecks.
  • Clock Speed: Higher clock speeds can improve performance, especially for applications requiring quick data processing.
  • TDP (Thermal Design Power): This indicates the GPU’s power consumption and heat output. Ensure your server can handle the thermal and power requirements of the GPU.

Read reviews, benchmark results, and expert opinions to understand how different GPUs perform in real-world scenarios.

5. Ask the Experts
Don’t hesitate to contact us, our experienced team is here to assist. They can offer valuable insights and recommendations based on your specific requirements and budget. Explain your use case in detail, and we can guide you towards the most suitable GPU options for our dedicated servers.

6. Consider Scalability and Plan for the Future
Your computational needs might evolve over time, so it’s wise to choose a GPU that can scale with your demands. Consider models that allow for future upgrades or the addition of more GPUs to your server. Investing in a scalable solution can save you money and hassle in the long run.

7. Find a Trustworthy Provider
Purchasing your GPU from a reputable vendor is crucial. Look for vendors with a proven track record of reliability and excellent customer support. A solid warranty can also provide peace of mind in case of any issues. Don’t solely focus on price; factor in the vendor’s reputation and after-sales service.


Stay tuned for our upcoming article, where we will guide you on how to pair your FlokiNET dedicated server with the ideal GPU for optimal performance. Feel free to reach out to FlokiNET’s experts at info@flokinet.is for personalized guidance and support in selecting the ideal GPU for your specific requirements.