how to increase graphic card slot for machine learning

Must Read: GDDR5 vs GDDR5X vs HBM2 vs GDDR6 Comparison Volta GPU Architecture Pascal-based GPUs can also be found in Notebooks or Laptops. Nvidia Pascal GPU architecture supports DirectX 12, OpenGL 4.6, Vulkan, OpenCL, SLI (for high-end GeForce cards), NVLink (for top workstation GPUs only), NVENC, G-Sync, GPU Boost 3.0, DisplayPort 1.4, HDMI 2.0b, CUDA Compute Capability 6.0 / 6.1 and is VR Ready.

Only a higher-end workstation graphics card, Nvidia Quadro GP100 comes with high bandwidth HBM2 memory.

Pascal GPU architecture supports GDDR5, GDDR5X, and HBM2 memory. Pascal GPU architecture brings significant improvements over the older architectures in terms of performance, power consumption (TDP), and heat generation. GeForce GTX 1050, GTX 1050 Ti, and GT 1030 are built on 14nm FinFET technology while others are built on the 16nmm technology process. Pascal GPUs are built on the 16nm / 14nm fabrication process and use CUDA Cores as their main pixel processing units and for Rasterization. The GeForce 10 series and Quadro P series graphics cards are built on the Pascal GPU architecture. Pascal is one of the most popular GPU architectures from Nvidia and the successor of Maxwell GPU architecture. But here I am going to make a comparison of Nvidia’s three modern GPU architectures, which are Pascal, Volta, and Turing based on their specifications, performance, usage & applications in the graphics industry. Fermi is different from Kepler, Pascal is different from Kelper, and so on. Also, two GPU architectures from the same GPU manufacturer are different in working, efficiency, and performance e.g. Examples of Nvidia GPU architectures are Fermi, Kepler, Pascal, Volta, and Turing whereas from AMD we have GCN (1.0, 2.0, 3.0), Polaris (GCN 4.0), and Vega. Every GPU manufacturer designs its own GPU architecture and GPU architectures of graphics cards from Nvidia and AMD are totally different in working, operation and naming.

How to increase graphic card slot for machine learning