![]() Sometimes the SGD takes one night to diverge, wasting whole night's work. Put in a bad learning rate you will watch the model die SLOWLY. Training neural net on CPU is just like watching a horror movie in 10x slow motion. Also, I am wondering how things have changed since I got my last desktop.Įven thought I have experimented with neural nets for a year or so, I have to confess that I have been training most of my models on CPUs. So why do I want to put up a clumsy, heavy machine now? Because I figured this is the way I probably can get the most computation power for the least upfront cost. I have not assembled any desktop PC for ages. I got my last desktop from work in 2011, when I found no one claims the lonely HP workstation in the corner. This is when you can use a lot of help from a powerful desktop computer sitting on your desk.ĭesktop PC is a bit out of fashion during the last few years, when everything slowly and steadily moves to the cloud. Even through the neural net / deep learning frameworks are much more sophisticated and easy to use than it used to be one year ago, you still need some (read: a lot) tuning toward a usable DL model. Just login and start right away with your favourite DeepLearning framework.As a data scientist, I do a lot of experimentation on models. Our machines come with preinstalled Linux OS configured with latest drivers and frameworks like Tensorflow, Keras, PyTorch and mxnet. The AIME T400 was first designed for our own deep learning application needs and evolved in years of experience in deep learning frameworks and customized PC hardware building. Tested with Real Life Deep Learning Applications They are perfectly balanced, so there are no performance bottlenecks.We optimize our hardware in terms of cost per performance, without compromising endurance and reliability. MLC Type: highest read and write speed - best suitable for high performance databases, data streaming and virtualizationĪll of our components have been selected for their energy efficiency, durability, compatibility and high performance.TLC Type: highest read and high write speed - best suitable for fast read/write file access.QLC Type: high read rates, average write speed - best suitable for reading of static data libraries or archieves.We offer following 3 class types of SSD to be configured: The AIME T400 can be configured with two NVMe SSDs, which are connected by PCI lanes directly to CPU and main memory. ![]() A high throughput and fast access times to the data are essential for fast turn around times. Up to 4TB High-Speed SSD Storageĭeep Learning is most often linked to high amount of data to be processed and stored. The available 64 PCI 3.0 lanes of the AMD Threadripper CPU allow highest interconnect and data transfer rates between the CPU and the GPUs.Ī large amount of available CPU cores can improve the performance dramatically in case the CPU is used for prepossessing and delivering of data to optimal feed the GPUs with workloads. The high-end AMD Threadripper CPU designed for worksations and servers delivers up to 32 cores with a total of 64 threads per CPU with an unbeaten price performance ratio. With the AIME T400 you can combine the power of 4 of those adding up to more then 400 Trillion Tensor FLOPS of AI performance. Which works with all popular deep learning frameworks. Supported by NVIDIA’s CUDA-X AI SDK, including cuDNN, TensorRT, and more than 15 other libraries. The AIME T400 relies on liquid cooled NVIDIA RTX 2080 TI GPUs, the best price performance GPU for Deep Learning.Įach NVIDIA RTX 2080 TI trains AI models with 544 NVIDIA Turing mixed-precision Tensor Cores delivering 107 Trillion Tensor FLOPS of AI performance and 11 GB of ultra-fast GDDR6 memory. The liquid cooled GPUs allow a higher packing of GPUs, with the AIME T400 up to 4 high-end GPUs can be packed in a system with the dimensions of a standard midi tower. ![]() 4 times NVIDIA RTX 2080 TI - Turing Power This setup keeps the system cooler, more performant, durable and far less noisy than an collection of many small fans for each component. The large radiators in the front and on top of the system are cooled by high durability noise reduced fans with a guaranteed operation of more than 10 years MTBF. The secured low operation temperature reduces stressful overheating for GPU and CPU silicons and allows that all components keep operating at their highest performance levels even under full load in 24/7 scenarios. High thermal conduction blocks and the far higher cooling capabilities of liquid compared to air keeps the operation temperature of all components well below 60☌. The elobareted cooling system of the AIME T400 covers the CPU and all GPUs with a stream of liquid cooling. AIME T400 - Liquid Cool Deep Learning Performance ![]()
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