NVIDIA Dominates The Market For Cloud AI Accelerators More Than You Think

Many companies want to compete with
NVIDIA
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
seems to be in the best position to capture share of cloud instance types with dedicated accelerators in 2019.

Dozens of dedicated AI accelerator chips are in development at companies like AWS, Graphcore, Gyrfalcon,
Intel
, Mythic and Wave Computing, plus FPGA chips at Intel, Xilinx and new entrant Achronix. Many of these chips are gunning to compete with NVIDIA to support cloud delivery of the deep learning models that underly consumer-facing services such as ad placement, retail recommendation engines, smart speakers and language translation.

Share of Instance Types

How far do NVIDIA’s competitors have to go to make a dent in NVIDIA’s general-purpose GPU business? In May 2019, the top four clouds deployed NVIDIA GPUs in 97.4% of Infrastructure-as-a-Service (IaaS) compute instance types with dedicated accelerators (Table 1). IaaS compute instance types define the specifications for each unique configuration of fractional server that clouds rent to IT customers.

The top four public clouds worldwide are Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure and Alibaba Cloud (also known as “Aliyun”). Canalys Cloud Analysis service estimates that these four clouds accounted for 62.3% of combined cloud IaaS and Platform-as-a-Service (PaaS) revenue in Q1 2019.

Cloud AI Accelerator Implications

What this means, in a practical sense, is that AI accelerator market entrants are not going to be competing with all of NVIDIA. They will each be competing with a specific NVIDIA product, such as NVIDIA’s older Tesla K80 or its recently introduced Tesla T4.

In May 2019, the top four clouds offered almost 12,000 instance types, of…

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