drivers, Nvidia video cards cannot deliver adequate features on some platforms and architectures given that the company only provides x86/x64 and ARMv7-A driver builds. As a result, support for 3D graphics acceleration in Linux on PowerPC does not exist, nor does support for Linux on the hypervisor-restricted PlayStation 3 console. Some users claim that Nvidia's Linux drivers impose artificial restrictions, like limiting the number of monitors that can be used at the same time, but the company has not commented on these accusations. In 2014, with Maxwell GPUs, Nvidia started to require firmware by them to unlock all features of its graphics cards. Up to now, this state has not changed and makes writing open-source drivers difficult. On 12 May 2022, Nvidia announced that they are opensourcing their GPU kernel drivers. They are still maintaining closed source userland utilities, hence making users still dependent on their proprietary software. Deep learning Nvidia GPUs are used in deep learning, and accelerated analytics due to Nvidia's API CUDA which allows programmers to utilize the higher number of cores present in GPUs to parallelize BLAS operations which are extensively used in machine learning algorithms. They were included in many Tesla vehicles before Elon Musk announced at Tesla Autonomy Day in 2019 that the company developed its own SoC and full self-driving computer now and would stop using Nvidia hardware for their vehicles. These GPUs are used by researchers, laboratories, tech companies and enterprise companies. In 2009, Nvidia was involved in what was called the "big bang" of deep learning, "as deep-learning neural networks were combined with Nvidia graphics processing units (GPUs)". That year, the Google Brain used Nvidia GPUs to create Deep Neural Networks capable of machine learning, where Andrew Ng determined that GPUs could increase the speed of deep-learning systems by about 100 times. DGX DGX is a line of supercomputers by Nvidia. In April 2016, Nvidia produced the DGX-1 based on an 8 GPU cluster, to improve the ability of users to use deep learning by combining GPUs with integrated deep learning software. It also developed Nvidia Tesla K80 and P100 GPU-based virtual machines, which are available through Google Cloud, which Google installed in November 2016. Microsoft added GPU servers in a preview offering of its N series based on Nvidia's Tesla K80s, each containing 4992 processing cores. Later that year, AWS's P2 instance was produced using up to 16 Nvidia Tesla