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Core config – The layout of the graphics pipeline, in terms of functional units. Over time the number, type, and variety of functional units in the GPU core has changed significantly; before each section in the list there is an explanation as to what functional units are present in each generation of processors.
The GeForce 30 series is a suite of graphics processing units (GPUs) developed by Nvidia, succeeding the GeForce 20 series.The GeForce 30 series is based on the Ampere architecture, which features Nvidia's second-generation ray tracing (RT) cores and third-generation Tensor Cores. [3]
Key elements include dedicated artificial intelligence processors ("Tensor cores") and dedicated ray tracing processors ("RT cores"). Turing leverages DXR, OptiX, and Vulkan for access to ray tracing. In February 2019, Nvidia released the GeForce 16 series GPUs, which utilizes the new Turing design but lacks the RT and Tensor cores.
NVIDIA Corporation (NASDAQ: NVDA) unveiled new gaming GPUs Tuesday based on the Ampere architecture.The Nvidia Analysts: BofA Securities analyst Vivek Arya reiterated a Buy rating on Nvidia and ...
The GeForce 20 series is a family of graphics processing units developed by Nvidia. [8] Serving as the successor to the GeForce 10 series, [9] the line started shipping on September 20, 2018, [10] and after several editions, on July 2, 2019, the GeForce RTX Super line of cards was announced.
Nvidia RTX (also known as Nvidia GeForce RTX under the GeForce brand) is a professional visual computing platform created by Nvidia, primarily used in workstations for designing complex large-scale models in architecture and product design, scientific visualization, energy exploration, and film and video production, as well as being used in mainstream PCs for gaming.
Tensor cores: A tensor core is a unit that multiplies two 4×4 FP16 matrices, and then adds a third FP16 or FP32 matrix to the result by using fused multiply–add operations, and obtains an FP32 result that could be optionally demoted to an FP16 result. [12] Tensor cores are intended to speed up the training of neural networks. [12]
The GPU having quick access to a high amount of L2 cache benefits complex operations like ray tracing compared to the GPU seeking data from the GDDR video memory which is slower. Relying less on accessing memory for storing important and frequently accessed data means that a narrower memory bus width can be used in tandem with a large L2 cache.