the best choice for deep learning in cloud
|Card||Price per hour2||FP32, TFLOPS (peak)||FP32 (ML benchmarks score)1||Memory, GB||Tensor cores||CUDA cores|
1. Based on average normalized GPU score for ResNet, Inception and AlexNet benchmarks. Normalization was performed to 2080Ti score (1 is 2080 Ti score).
2. The minimum market price per 1 GPU on demand, taken from public price lists of popular cloud and hosting providers. Information is current as of June 2020.
* Benchmarks are made on instances with 1 GPU, 16GB RAM, 4vCPU and fast storage with similar IOPS and bandwidth rate.
** All multiplicators are calculated in proportion to 2080Ti.
*AMD, and the AMD Arrow logo, AMD EPYC and combinations thereof are trademarks of Advanced Micro Devices, Inc.
With 2nd generation AMD processors you can allocate 64 vCPU and 10 GPU in one Docker container.
Fast, reliable storage for your datasets and trained models, runtime-extensible up to 4TB.
DDR4 ECC 2.9Ghz memory with flexible allocation up to 384GB.
Disk space will be provided to store Docker image files for containers of your pod.
Fast flexible storage for your datasets. To reduce your costs you can load data via SSH first and run pod with GPUs after.
A container file system provides a temporary fast storage for each container in your pod. Its capacity cannot be changed. You pay only if your application use it.
*1GB = 1024MB