Best GPU for Machine Learning 2022 – Are you searching for new products? than you are at the correct page. As we have done our research for you guys. We have also written buyer’s guide for you.
Best Graphics Card Company 2022
#01 – 2U, 8 x NVIDIA GeForce RTX 2080 TI 11GB GPU Gigabyte G291-280 Deep Machine Learning AI Rendering HPC Graphics Rack Server Computer System for High Density Enterprise (Dual Xeon 3104 CPU, 96GB ECC RAM)
No products found.
#02 – Hydra V Rev. B 20 GPU Frame Rack for Learning/Mining/Rendering Servers, E-ATX and 5 PSU Ready
- No other GPU case offers this much space! Designed to fit wider cards (3-slots), larger motherboards (E-ATX) and lots of power supplies (up to 5).
- Great for motherboards like Asus B250 Mining Expert (19 GPU mining) or Asus Z10PE WS (dual CPU rendering).
- Support: 1 SSD, 1 HDD, 1 E-ATX Mobo, 5 PSU, 20 3-slot cards, and 10 120 mm fans; Fully assembled dimension is 29.25 x 23 x 14.25 inches.
- Easily add (or remove) fans using the fan holder plates, and reverse GPU orientation with 20x or 16x mounting hole patterns.
- Comes in pre-manufactured assemblies to minimize the number of tiny screws you need to put together.
#03 – 2U, 8 x NVIDIA GeForce RTX 2080 TI 11GB GPU Gigabyte G291-280 Deep Machine Learning AI Rendering HPC Graphics Rack Server Computer System for High Density Enterprise (Dual Xeon 3104 CPU, 96GB ECC RAM)
No products found.
#04 – youyeetoo NanoPi M4V2 RK3399 SoC Based ARM Board Compatible with Ports and interfaces for RPi B3+ Supports Android 8.1 and Ubuntu Desktop 18.04 for deep Learning，Game Machines, Blockchain
- The NanoPi M4 is a RK3399 SoC based ARM board. It has the same form of factor as the RPi B3+ and has ports and interfaces compatible with RPi B3+ too. On a 85 X 56 mm compact board there are rich hardware resources.
- The NanoPi M4 has an onboard 2.4G & 5G dual-band WiFi and Bluetooth module, four USB3.0 Type A host ports, one Gbps Ethernet port, one HDMI 2.0 Type A port, one 3.5mm audio jack and one Type-C port. In addition it has a RPi compatible 40-pin connector, dual MIPI-CSI camera interface, PClex2, USB 2.0, eMMC socket, RTC port and etc.
- The NanoPi M4 has 2GB DDR3 RAM. Itcan be booted from either a TF card or an external eMMC module. The NanoPi M4 supports Ubuntu Desktop 18.04(64-bit), Lubuntu 16.04(32-bit), Ubuntu Core 18.04(64-bit), Android 8.1 and Lubuntu Desktopwith GPU and VPU acceleration.
- With these rich resources and powerful performance it can be widely used in applications of machine learning, Al, deep learning, robots, industrial control, industrial cameras, advertisement machines, game machines, blockchain and etc.
#05 – Deep Learning DevBox – Intel Core Extreme X299 for CUDA, AI Inference, Machine Learning (i7-9800X 8-Core Processor, 2 x Tesla T4)
- Fully Configured with Ubuntu and Widely Used Deep Learning Frameworks
- Includes Intel Core X-Series Processor, Nvidia GPGPU, 64GB Memory, 1TB NVMe M.2 Boot Drive, and 4TB 3.5" Enterprise HDD Storage Drive
- Supports up to four GPGPU, Supports up to 128GB Memory, Supports up to 8 x drives
- Configured with Ubuntu 18.04 LTS, NVIDIA CUDA, TensorFlow, Caffe2, PyTorch, Mxnet, DL4J, Docker, Keras
- Case Dimensions: 415 x 332 x 458m, 16.34" x 13.07" x 18.03" in inches
#06 – NVIDIA Jetson Nano Developer Kit (945-13450-0000-100
- The NVIDIA Jetson Nano Developer Kit delivers the compute performance to run modern AI workloads at unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing.
- The developer kit can be powered by micro-USB and comes with extensive I/Os, ranging from GPIO to CSI. This makes it simple for developers to connect a diverse set of new sensors to enable a variety of AI applications. And it is incredibly power-efficient, consuming as little as 5 watts.
- Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA, cuDNN, and TensorRT software libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more. The software is even available using an easy-to-flash SD card image, making it fast and easy to get started.
- The same JetPack SDK is used across the entire NVIDIA Jetson family of products and is fully compatible with NVIDIA’s world-leading AI platform for training and deploying AI software. This proven software stack reduces complexity and overall effort for developers.
#07 – NVIDIA 945-82771-0000-000 Jetson TX2 Development Kit
- Developer Kit for the Jetson TX2 module. Includes Jetson TX2 module with NVIDIA Pascal GPU, ARM 128-bit CPUs, 8 GB LPDDR4, 32 GB eMMC, Wi-Fi and BT Ready
- NVIDIA Pascal Embedded module loaded with 8GB of memory and 58.4 GB/s of memory bandwidth
- Wi-Fi and BT Ready
#08 – Latest Jetson Nano Developer Kit (B01) Small Powerful Computer for AI Development Board + 7 inch IPS Touch HDMI Screen LCD Display Micro Card 64GB Camera Module @XYGStudy (PackC)
- Part Number: Jetson Nano Developer Kit Package C
- NVIDIA Jetson Nano Developer Kit is a small, powerful computer that lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. All in an easy-to-use platform that runs in as little as 5 watts.
- Jetson Nano delivers 472 GFLOPS for running modern AI algorithms fast, with a quad-core 64-bit ARM CPU, a 128-core integrated NVIDIA GPU, as well as 4GB LPDDR4 memory. It runs multiple neural networks in parallel and processes several high-resolution sensors simultaneously.
- Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), CUDA, cuDNN, and TensorRT software libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more. The SDK also includes the ability to natively install popular open source Machine Learning (ML) frameworks such as TensorFlow, PyTorch, Caffe / Caffe2, Keras, and MXNet, enables the developers to integrate their favorite AI model / AI framework into products easily.
- GPU: 128-core Maxwell GPU, CPU: quad-core ARM Cortex-A57 CPU, Memory: 4GB 64-bit LPDDR4, Storage: Micro SD card slot (requires an external minimum 16G TF card)
#09 – NVIDIA Jetson AGX Xavier Developer Kit (32GB)
- Newly updated version with an additional 16GB of memory for a total of 32GB of 256-bit wide LPDDR4X memory.
- NVIDIA Jetson Xavier is an AI computer for Autonomous Machines with the performance of a GPU workstation in under 30W
- The Jetson Xavier Developer Kit with Jetson Xavier module and reference carrier board is the fastest way to start prototyping with robots, drones and other autonomous machines
- Visit the NVIDIA Jetson developer site for the latest software, documentation, sample applications, and developer community information
- System Ram Type: Ddr Dram
#10 – FriendlyElec NanoPC-T4 Open Source RK3399 ARM Development Board LPDDR3 RAM 4GB Gbps Ethernet,Support Android and Ubuntu, AI and deep Learning
- Dual Camera Interface & Dual 4K output: Supports simultaneous input of dual camera data annd dual display output.,Perfect Platform for VR, AI,Machine Learning anddeep vision Applications. FriendlyElec's Android 8.1 suppots display rotation. You can rotate display by 0*/90*/180*/270* degrees
- M.2 NVME PCle x4: The NanoPC-T4's M.2 M-Key PClex4 interface has powerful expansion capabilities. It supports SSD storage expansion and expansion interfaces including STAT, USB3.0/3.1, 1G/10Gbps Ethernet, high speed WiFi and etc
- SuperSpeed USB Interface: USB 3.0 Type-A,10 times faster than USB 2.0，Up to 5.0Gbps; USB Type-C is a dual-role port and it supports VESA DisplayPort Alt Mode for USB Type C standard
- Docker on Ubuntu,Open Source on Github.: Docker is a platform for developers and sysadmins to develop, deploy, and run applications with containers.And Docker is now supported in all three FriendlyElec's systems for RK3399: FriendlyDesktop, FriendlyCore and Lubuntu Desktop
Related Tags: Best GPU for Machine Learning 2022