However, this is a pretty rare edge case. If you are creating your own model architecture and it simply can't fit even when you bring the batch size lower, the V100 could make sense. If you absolutely need 32 GB of memory because your model size won't fit into 11 GB of memory with a batch size of 1.If you're not sure if you need FP64, you don't. If you're doing Computational Fluid Dynamics, n-body simulation, or other work that requires high numerical precision (FP64), then you'll need to buy the Titan V or V100s. There are, however, a few key use cases where the V100s can come in handy: If you're not AWS, Azure, or Google Cloud then you're probably much better off buying the 2080 Ti.
The RTX and GTX series of cards still offers the best performance per dollar. How can the 2080 Ti be 80% as fast as the Tesla V100, but only 1/8th of the price? The answer is simple: NVIDIA wants to segment the market so that those with high willingness to pay (hyper scalers) only buy their TESLA line of cards which retail for ~$9,800. 2080 Ti vs V100 - is the 2080 Ti really that fast? Under this evaluation metric, the RTX 2080 Ti wins our contest for best GPU for Deep Learning training. We then averaged the GPU's speedup over the 1080 Ti across all models:įP32 and FP16 performance per $. Throughput of each GPU on various models raw data can be found here. Speedup is a measure of the relative performance of two systems processing the same job. We divided the GPU's throughput on each model by the 1080 Ti's throughput on the same model this normalized the data and provided the GPU's per-model speedup over the 1080 Ti. Performance of each GPU was evaluated by measuring FP32 and FP16 throughput (# of training samples processed per second) while training common models on synthetic data. Tesla V100 benchmarks were conducted on an AWS P3 instance with an E5-2686 v4 (16 core) and 244 GB DDR4 RAM. HardwareĪ Lambda deep learning workstation was used to conduct benchmarks of the RTX 2080 Ti, RTX 2080, GTX 1080 Ti, and Titan V. You can view the benchmark data spreadsheet here.
This chip will not reach OEM market sooner than Q1 2017, so (G)P100 based might not launch as soon as we expect. Jen-Hsun Huang announced that P100 is in volume production today. The chip has some amazing specs and performance. It’s the most complex project NVIDIA has ever built. TESLA P100 has die size of 600m 2, which is exactly how I predicted in my last article about Pascal.
NVIDIA decided to go full throttle on new FinFET 16nm architecture by designing the biggest possible chip. TESLA P100 becomes the first Pascal GPU and the first HBM2 based graphics card. The GPU itself only has 15.3 billion transistors. Total number of transistors comes from GPU, HBM and interposer. While 150 billion transistors seems like a large number, this is not just the GPU. NVIDIA announces first Pascal-based graphics card with staggering 150 billion transistors (the full package).