High-performance GPUs - reach the next level of computing power

GPU for machine learning (ML) and AI-based processing of large data volumes.

In Bineros public cloud service, we offer graphics card acceleration (GPU acceleration). Our solution is based on Nvidia's Ampere series of GPUs, optimized for data centers and computing.

GPU graphics card. GPU grafikkort
  • Flexibility i n a cloud infrastructure
  • Acceleration of heavy calculations
  • Option for NVMe-based  storage

Why GPU in advanced data analysis?

For the most demanding areas of use in data processing and analysis, new and more efficient tools are needed. A GPU (Graphics Processing Unit) has historically been used to perform calculations to render a 3D image on a screen. On a modern GPU, intended for data centers, a user can instead perform calculations in programs that traditionally used a processor (CPU), which provides several major advantages.

Using GPUs is fast becoming a new standard for highly parallel computing tasks in science and technology. With a GPU, you can train your AI models faster and process huge amounts of data in a short amount of time. A performance improvement on your application of 50-150x depending on the application is often reasonable compared to doing the same work on a CPU. This enables new areas of use such as image analysis in real time (e.g. for face recognition) or machine learning via neural networks.

GPU vs CPU comparison. GPU vs CPU jämförelse

What is the difference between GPU and CPU?

A modern GPU typically contains over a thousand processor cores and is therefore extremely good at computational loads where you can do many tasks at the same time (parallel load instead of serial load).

A consequence of this is very high bandwidth through the GPU, which in turn places high demands on its memory, which is normally significantly faster than normal RAM memory. The result of these differences is a device that differs significantly from the "multitasking" CPU by focusing on many, simpler tasks of the same type.

How much do gpu instances cost?

High performance with GPU
High performance with NVMe
High performance with GPU & NVMe
hp.4x8-gpu44 Core 8GB RAM4844.12296 SEK2968.53 SEK
hp.8x24-gpu48 Core 24GB RAM82445.62086 SEK4047.02 SEK
hp.12x64-gpu412 Core 64GB RAM126448.61907 SEK6205.73 SEK
hp.4x8-gpu84 Core 8GB RAM4885.16463 SEK3718.53 SEK
hp.8x24-gpu88 Core 24GB RAM82486.666253 SEK4799.70 SEK
hp.12x64-gpu812 Core 64GB RAM126489.66074 SEK6955.73 SEK
hp.4x8-gpu244 Core 8GB RAM482410.37296 SEK7468.53 SEK
hp.8x24-gpu248 Core 24GB RAM8242411.87086 SEK8547.02 SEK
hp.12x64-gpu2412 Core 64GB RAM12642414.86907 SEK10705.73 SEK
hp.4x8-nvme504 Core 8GB RAM481.193692 SEK859.45 SEK
hp.8x24-nvme508 Core 24GB RAM8242.766487 SEK1991.87 SEK
hp.12x64-nvme5012 Core 64GB RAM12645.914608 SEK4258.51 SEK
hp.4x8-nvme2504 Core 8GB RAM481.777020 SEK1279.45 SEK
hp.8x24-nvme2508 Core 24GB RAM8243.349825 SEK2411.87 SEK
hp.12x64-nvme25012 Core 64GB RAM12646.497935 SEK4678.51 SEK
Specifikation CPU RAM GPU Kostn./tim Kostn./mån
hp.4x8-gpu4-nvme50 4 Core 8GB RAM 4 8 4 4.26185 SEK 3068.53 SEK
hp.8x24-gpu4-nvme508 Core 24GB RAM82445.75975 SEK4147.02 SEK
hp.12x64-gpu4-nvme5012 Core 64GB RAM126448.75796 SEK6305.73 SEK
hp.4x8-gpu8-nvme504 Core 8GB RAM4885.30351 SEK3818.53 SEK
hp.8x24-gpu8-nvme508 Core 24GB RAM82486.80142 SEK4897.02 SEK
hp.12x64-gpu8-nvme5012 Core 64GB RAM126489.79963 SEK7055.73 SEK
hp.4x8-gpu24-nvme504 Core 8GB RAM482410.51185 SEK7568.53 SEK
hp.8x24-gpu24-nvme508 Core 24GB RAM8242412.00975 SEK8647.02 SEK
hp.12x64-gpu24-nvme5012 Core 64GB RAM12642415.00796 SEK10805.73 SEK
hp.4x8-gpu4-nvme2504 Core 8GB RAM4844.8174 SEK3468.53 SEK
hp.8x24-gpu4-nvme2508 Core 24GB RAM82446.31531 SEK4547.02 SEK
hp.12x64-gpu4-nvme25012 Core 64GB RAM126449.31351 SEK6705.73 SEK
hp.4x8-gpu8-nvme2504 Core 8GB RAM4885.85907 SEK4218.53 SEK
hp.8x24-gpu8-nvme2508 Core 24GB RAM82487.35697 SEK5297.02 SEK
hp.12x64-gpu8-nvme25012 Core 64GB RAM1264810.35518 SEK7455.73 SEK
hp.4x8-gpu24-nvme2504 Core 8GB RAM482411.0674 SEK7968.53 SEK
hp.8x24-gpu24-nvme2508 Core 24GB RAM8242412.56531 SEK9047.02 SEK
hp.12x64-gpu24-nvme25012 Core 64GB RAM12642415.56351 SEK11205.73 SEK

Get started with GPU today!

Binero's public cloud platform is used by everything from large enterprises to fast-moving startups. It's quick and easy to get started and test! The GPU service is easy to use and we'll guide you along the way.

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How are our customers using the GPU?

AktivBo is a data-driven knowledge and platform company for the real estate market. They offer their customers comprehensive support in the work with customer-driven efficiency and profitability improvements. Through data collection, data-driven analysis and actionable insights, the basis for managing the property companies' operations is created.

AktivBo sought a powerful and easy-to-use European cloud service based on open standards to be able to train and operate its machine learning models. Today, AktivBo uses Binero's public cloud with GPU acceleration for AI-based analysis of large data volumes.

Learn more about AktivBo's case.

What does GPU mean for different use cases?

With a GPU, you can work through a large data set faster and more efficiently, alternatively do many smaller operations on a data set and therefore make deeper analyzes or more realistic models. This gives great upside in areas of use such as:

  • Advanced analysis - With a GPU, the ability to perform deeper analysis on larger data sets arises from the superior computing power.
  • Machine learning (AI/ML) - To train neural networks, a very large data set is required on which smaller operations must be applied. Our GPUs provide more than enough power and speed to optimally generate complex models and training at high speed. This improves the predictions and decisions of an algorithm.
  • Scientific research - For example, through automated analysis of image material. Thanks to the high speed, researchers can save a lot of valuable time. Large amounts of data are processed and analyzed quickly and make the data visible. Unstructured data can be easily classified and summarized.
  • Virtual reality - By considering more parameters than is possible on a CPU, a more realistic and functional environment can be built.

Offloading calculations to a GPU also makes the processor's capacity available for other tasks, which in turn improves the overall speed of the server and application.

Colleagues discussing projects. Kollegor diskuterar projekt.
A lot of Kubernetes containers. En massa kubernetes-konteinrar.

What does binero's GPU solution look like?

Binero's public cloud service now also offers graphics card acceleration (GPU acceleration). Our solution is based on Nvidia's Ampere series of GPUs, optimized for data centers and computing. We provide multiple sizes of GPU memory so our users can find the most cost-effective solution for their needs.

The possibility of using storage based on NVMe is also available for these instances to optimize IO-intensive and latency-sensitive buffering of data during heavy calculations. Binero offers GPUs with the highest flexibility in a transparent "pay-as-you-go" model where you pay by the hour.

Cloud-based GPU is an efficient solution to complex tasks and has many advantages:

  • Acceleration of heavy calculations
  • Flexibility in a cloud infrastructure
  • High performance computing power
  • Low latency connection between servers in the platform
  • Possibility of NVMe-based storage

Contact us

Got questions about GPU? Consult our specialist or fill in the form and we will get back to you directly.

Ask our specialist

emil cloud expert and customer manager of binero clients

Emil Rydin

Customer manager


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