In this information explosion era, artificial intelligence, robotics, and industry 4.0 is the cornerstone of the success of advanced opportunities in any industry. The ceaseless progress of technology forces innovative opportunities to be generated. Robust sensors, secure communication, advanced video processing, and image solutions enable every industry to broaden new territories and improve operational accuracy in order to elevate human beings lives quality. This is the moment when AI inference and machine learning (ML) come to rescue. These two can contribute a lot when dealing with huge amount of data, which leads to another inevitable trend: speedy computing. Because of the growing demand for complicated AI-enabled services to deal with huge amount of information such as image and voice recognition, facial identification and DNA sequence process, collecting, storing and processing together becomes a triple must for data analysis. Therefore, in order to manage a colossal amount of data and information, a compelling CPU-GPU configuration becomes dominant.In this information explosion era, artificial intelligence, robotics, and industry 4.0 is the cornerstone of the success of advanced opportunities in any industry. The ceaseless progress of technology forces innovative opportunities to be generated. Robust sensors, secure communication, advanced video processing, and image solutions enable every industry to broaden new territories and improve operational accuracy in order to elevate human beings lives quality. This is the moment when AI inference and machine learning (ML) come to rescue. These two can contribute a lot when dealing with huge amount of data, which leads to another inevitable trend: speedy computing. Because of the growing demand for complicated AI-enabled services to deal with huge amount of information such as image and voice recognition, facial identification and DNA sequence process, collecting, storing and processing together becomes a triple must for data analysis. Therefore, in order to manage a colossal amount of data and information, a compelling CPU-GPU configuration becomes dominant.
7Starlake offers an extensive range of multiple GPUCPU combinations dedicated servers like 1CPU+1GPU up to 2CPU+4 GPU which are customised-built for massive parallel computing environments and today’s most demanding HPC and hyper scale data centre workloads for any high performance demanding tasks. They are perfectly adaptive for multi-functional use as well. These computers can process diverse vision sensor data simultaneously, which provides a high-performance solution to end-users.
I11C-G2060S is a high CUDA Core GPGPU computer that is designed to fulfil heavy duty computing demand edge AI inference system. Equipped with NVIDIA RTX2060S and Intel 9th Gen. i7-9700TE CPU, this powerful processor can easily handle multi-sensor fusion task from data collection, data analysis to application. Some of its features as follow.
Key Features
- Intel Gen. 9th Core i/ Xeon E Processor
- NVIDIA RTX2060S (8GB GDDR6, 2176 CUDA)
- DDR4 2666 MHz up to 64GB
- Support NVMe PCIe 3.0 For Fast Storage
- 6 x USB, 2 x LAN, 2xDP, 1x HDMI
- 16V~34V 500W DC-DC
- Extended Operating Temperature -20 to +55°C
1.Intel 9th Gen. i7-9700TE
Intel’s i7-9700TE CPU (8 Cores, 1.8GHz Turbo up to 3.8GHz) delivers the efficient performance to strengthen multiple workloads. Compare to 7th Generation CPU Processor, Coffeelake-R platform provides up to 36% better integer multi-threaded compute intensive application performance. With this flexible platform which is designed for intelligent devices and a mature ecosystem, I11S-G2060S is able to accelerate the development of your value-add solutions so that you can quickly put your ideas into action.
2. NVIDIA GeForce RTX 2060 Super
i11C-G2060S supports NVIDIA® GeForce® RTX 2060 SUPER™ which is powered by the NVIDIA Turing™ architecture, bringing superfast all-around performance and graphics to every gamer and creator. It’s time to gear up and get super powers.
3. Support NVMe PCIe 3.0 For Fast Storage
Compared with a SATA SSD drive, an NVMe-based drive can write to disk up to 4x faster that can perfectly deal with data intensive AI applications. Moreover, seek times are up to 10x faster. It is worth noting that NVMe is not merely fast because it connects via PCIe interfaces; there is also a lot of engineering on the drives themselves especially pertaining to how it organises read/write requests. NVMe-based storage, on the other hand, supports multiple I/O queues, with a theoretical maximum of 64,000 queues, each permitting 64,000 entries for a grand total of 4.096 billion entries. These are intelligently shaped by the system’s characteristics and predicted workload, rather than some kind of hard-coded one-size-fits-all solution.
GPU Computers
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