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.
- flexibility in 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.
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?
|hp.4x8-gpu4||4 Core 8GB RAM||4||8||4||4.12296 SEK||2968.53 SEK|
|hp.8x24-gpu4||8 Core 24GB RAM||8||24||4||5.62086 SEK||4047.02 SEK|
|hp.12x64-gpu4||12 Core 64GB RAM||12||64||4||8.61907 SEK||6205.73 SEK|
|hp.4x8-gpu8||4 Core 8GB RAM||4||8||8||5.16463 SEK||3718.53 SEK|
|hp.8x24-gpu8||8 Core 24GB RAM||8||24||8||6.666253 SEK||4799.70 SEK|
|hp.12x64-gpu8||12 Core 64GB RAM||12||64||8||9.66074 SEK||6955.73 SEK|
|hp.4x8-gpu24||4 Core 8GB RAM||4||8||24||10.37296 SEK||7468.53 SEK|
|hp.8x24-gpu24||8 Core 24GB RAM||8||24||24||11.87086 SEK||8547.02 SEK|
|hp.12x64-gpu24||12 Core 64GB RAM||12||64||24||14.86907 SEK||10705.73 SEK|
|hp.4x8-nvme50||4 Core 8GB RAM||4||8||1.193692 SEK||859.45 SEK|
|hp.8x24-nvme50||8 Core 24GB RAM||8||24||2.766487 SEK||1991.87 SEK|
|hp.12x64-nvme50||12 Core 64GB RAM||12||64||5.914608 SEK||4258.51 SEK|
|hp.4x8-nvme250||4 Core 8GB RAM||4||8||1.777020 SEK||1279.45 SEK|
|hp.8x24-nvme250||8 Core 24GB RAM||8||24||3.349825 SEK||2411.87 SEK|
|hp.12x64-nvme250||12 Core 64GB RAM||12||64||6.497935 SEK||4678.51 SEK|
|hp.4x8-gpu4-nvme50||4 Core 8GB RAM||4||8||4||4.26185 SEK||3068.53 SEK|
|hp.8x24-gpu4-nvme50||8 Core 24GB RAM||8||24||4||5.75975 SEK||4147.02 SEK|
|hp.12x64-gpu4-nvme50||12 Core 64GB RAM||12||64||4||8.75796 SEK||6305.73 SEK|
|hp.4x8-gpu8-nvme50||4 Core 8GB RAM||4||8||8||5.30351 SEK||3818.53 SEK|
|hp.8x24-gpu8-nvme50||8 Core 24GB RAM||8||24||8||6.80142 SEK||4897.02 SEK|
|hp.12x64-gpu8-nvme50||12 Core 64GB RAM||12||64||8||9.79963 SEK||7055.73 SEK|
|hp.4x8-gpu24-nvme50||4 Core 8GB RAM||4||8||24||10.51185 SEK||7568.53 SEK|
|hp.8x24-gpu24-nvme50||8 Core 24GB RAM||8||24||24||12.00975 SEK||8647.02 SEK|
|hp.12x64-gpu24-nvme50||12 Core 64GB RAM||12||64||24||15.00796 SEK||10805.73 SEK|
|hp.4x8-gpu4-nvme250||4 Core 8GB RAM||4||8||4||4.8174 SEK||3468.53 SEK|
|hp.8x24-gpu4-nvme250||8 Core 24GB RAM||8||24||4||6.31531 SEK||4547.02 SEK|
|hp.12x64-gpu4-nvme250||12 Core 64GB RAM||12||64||4||9.31351 SEK||6705.73 SEK|
|hp.4x8-gpu8-nvme250||4 Core 8GB RAM||4||8||8||5.85907 SEK||4218.53 SEK|
|hp.8x24-gpu8-nvme250||8 Core 24GB RAM||8||24||8||7.35697 SEK||5297.02 SEK|
|hp.12x64-gpu8-nvme250||12 Core 64GB RAM||12||64||8||10.35518 SEK||7455.73 SEK|
|hp.4x8-gpu24-nvme250||4 Core 8GB RAM||4||8||24||11.0674 SEK||7968.53 SEK|
|hp.8x24-gpu24-nvme250||8 Core 24GB RAM||8||24||24||12.56531 SEK||9047.02 SEK|
|hp.12x64-gpu24-nvme250||12 Core 64GB RAM||12||64||24||15.56351 SEK||11205.73 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.
All new accounts are topped up with 1000 SEK to use resources for.
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.
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.
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
Got questions about GPU? Consult our specialist or fill in the form and we will get back to you directly.