On the journey of exploring deep learning, the performance of GPU is the key to the speed of progress. I had tried multiple platforms, attempting to find the "computing power engine" that could carry my deep learning dreams. However, the reality often fell short of expectations. Stuttering and latency were like shadows, restricting the speed of model training. When dealing with large-scale datasets and complex network architectures, the computing efficiency of these platforms seemed inadequate, and I was constantly tormented by stuttering and latency. Even more troublesome was that video memory management also became a major obstacle on my way forward. When handling tasks rich in sequential data such as video action recognition, the GPU video memory of common platforms was often stretched to the limit. When processing batches of high-definition video frames in long sequences with 16GB or 24GB of video memory, video memory overflow became a common occurrence. It was not until I encountered Burncloud (https://www.burncloud.com/835.html) that I truly unlocked the door to efficient deep learning.
Its computing efficiency is truly astonishing. When I trained a conventional convolutional neural network for image classification on a certain commonly used old cloud platform, the average time for a single iteration was 25 minutes. However, the NVIDIA A100 GPU equipped on Burncloud, with its 6,912 CUDA cores combined with an advanced architecture, reduced the iteration time for the same model and dataset to around 4 minutes. The efficiency improvement was more than 5 times, as if a high-speed motor had been installed for model training, rapidly pushing the loss function towards the optimal value.
Video memory management is also outstanding. When handling tasks rich in sequential data such as video action recognition, the GPU of common platforms usually has 16GB or 24GB of video memory. When processing batches of high-definition video frames in long sequences, there is a more than 70% probability of encountering video memory overflow. Burncloud is different. With large-capacity video memory starting from 40GB and reaching up to 80GB, it can easily accommodate the features of massive video frames. The whole training process is as stable as a mountain, and even complex 3D convolutional long short-term memory networks can set sail smoothly, ensuring that the exploration of deep learning does not run aground.
Multi-GPU collaboration is also a highlight of Burncloud. In distributed training for language models of the GPT scale, other platforms are limited by internal interconnection bandwidth, and the data synchronization latency between cards exceeds 50 microseconds, with a computing power utilization rate of less than 60%. Burncloud relies on the NVLink ultra-high-speed channel to reduce the latency to within 10 microseconds. The computing power of multiple cards can be expanded almost linearly, and the utilization rate exceeds 90%. It's like a group of well-trained elite teams working together to charge forward and overcome complex model problems. I sincerely recommend it to my fellow professionals. Come to Burncloud and press the accelerator button for your scientific research and projects!