For reference, if there is no TensorFLOPS value, we will provide the largest known deep learning performance with any precision. Tensor Core is only available for "Volta" GPU or newer. NVIDIA now measures Tensor Core's GPU through a new deep learning performance indicator: a new unit called TensorTFLOPS. It combines two FP16 units (converted into full-precision products) with FP32 accumulation operations-this is a precise operation used in deep learning training calculations.
TESLA K80 FP64 PROFESSIONAL
*The exact value depends on PCI-Express or SXM2 SKU TensorFLOPS and deep learning performanceĪ new professional Tensor Core unit was launched with the "Volta" GPU. Half-precision (16-bit) floating point performance The following is a comparison of the performance of half-precision floating-point calculations between GeForce and Tesla/Quadro GPUs: NVIDIA GPU model Although all NVIDIA "Pascal" and later GPUs support FP16, the performance is significantly reduced on many gaming-centric GPUs. This was the standard for deep learning/artificial intelligence computing in the past however, deep learning workloads have moved to more complex operations (see TensorCores below). In the "Pascal" GPU, support for half-precision FP16 operations was introduced. Some applications do not require high precision (for example, neural network training/inference and some HPC use). *The exact value depends on PCI-Express or SXM2 SKU FP16 16-bit (half precision) floating point calculation The following is a comparison of double-precision floating-point calculation performance between GeForce and Tesla/Quadro GPUs: NVIDIA GPU modelĭouble precision (64-bit) floating point performance Although almost all NVIDIA GPU products support single-precision and double-precision calculations, on most consumer-grade GeForce GPUs, the performance of double-precision values is much lower.
Less accurate values are called single precision (32 bits).
These larger values are called double precision (64 bits). In these applications, the data is represented by a value twice as large (using 64-bit binary bits instead of 32-bit). Many applications require higher precision mathematical calculations. FP64 64-bit (double precision) floating point calculation Professional Tesla and Quadro GPUs have many functions. However, it is wise to remember the differences between products. The consumer product line of GeForce GPUs (especially the GTX Titan) may be attractive to those running GPU-accelerated applications. All NVIDIA GPUs support general-purpose computing (GPGPU), but not all GPUs provide the same performance or support the same features. This resource was prepared by Microway based on data provided by NVIDIA and trusted media sources.
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