Many companies want to compete with NVIDIA NVDA in the cloud. They all want a chunk of the AI-based “Inference-as-a-Service” applications that NVIDIA has enabled over the past few years. Gaining significant market share against NVIDIA is going to be more difficult than most of them realize. Xilinx XLNX seems to be in the best position to capture share of cloud instance types with dedicated accelerators in 2019.
AMD is still following distantly behind NVIDIA’s GPU computing software enablement and marketing. AMD is able to engage with open source efforts like the Singularity high-performance container runtime with OCm GPU computing platform and HIP translation for NVIDIA CUDA code to C++. But these efforts have not moved the needle for AMD's GPUs in larger cloud service providers' share of dedicated accelerator instance types.
NVIDIA is not Sitting Still
On June 17, NVIDIA announced it will make its full stack of AI and high-performance computing (HPC) software available to the Arm ecosystem by the end of 2019, including Ampere’s eMAG, Huawei’s Kunpeng (given a resolution to international trade disputes) and Marvell’s (formerly Cavium) ThunderX series of server processors. NVIDIA already supports x86 (AMD and Intel) and POWER (IBM) processor architectures. Adding Arm support will complete NVIDIA’s coverage of all the server processor architectures currently (publicly) planned for cloud deployment over the next few years.