Stellar Performance
Nvidia announced Q2 revenues of $3.12 billion a rise of 40% from 2017. Performance gains came from the gaming segment comprising graphics chips bundled with personal computers. Data center business grew 83% with demand from Google and Amazon for running computations in the cloud. Disappointing was the fall in demand for digital currency chips, leading to revenue decline of 54% from last year.
Competition from "Custom Silicon"
Competition may come from a number of areas. Google and Baidu are investing in custom silicon, with Google's hardware projects run by former Motorola president, Rick Osterloh. For machine learning specifically, Google has a Tensor Processing Unit (TPU) to run neural networks at scale. In the automotive client space, Nvidia's autonomous driving chipset, Xavier, is under threat as Tesla aims to adopt its own machine learning silicon.
Traditional Competitors
Advanced Micro Devices is planning to release graphics chips for machine learning based on 7nm technology next year. Intel is also wanting to up its game, hiring AMD chief architect, Raja Koduri, to head up its graphics card business set to deliver product in two years. Nvidia will likely respond by upping the stakes in research and development. Expenses could run to $870m in Q3.
Keeping it Real
Nvidia's Jensen Huang presents at the SIGGRAPH Graphics Technology Conference (GTC). This builds real-time awareness of the company's latest product innovations and drives fan following.
Nvidia announced Q2 revenues of $3.12 billion a rise of 40% from 2017. Performance gains came from the gaming segment comprising graphics chips bundled with personal computers. Data center business grew 83% with demand from Google and Amazon for running computations in the cloud. Disappointing was the fall in demand for digital currency chips, leading to revenue decline of 54% from last year.
Competition from "Custom Silicon"
Competition may come from a number of areas. Google and Baidu are investing in custom silicon, with Google's hardware projects run by former Motorola president, Rick Osterloh. For machine learning specifically, Google has a Tensor Processing Unit (TPU) to run neural networks at scale. In the automotive client space, Nvidia's autonomous driving chipset, Xavier, is under threat as Tesla aims to adopt its own machine learning silicon.
Traditional Competitors
Advanced Micro Devices is planning to release graphics chips for machine learning based on 7nm technology next year. Intel is also wanting to up its game, hiring AMD chief architect, Raja Koduri, to head up its graphics card business set to deliver product in two years. Nvidia will likely respond by upping the stakes in research and development. Expenses could run to $870m in Q3.
Keeping it Real
Nvidia's Jensen Huang presents at the SIGGRAPH Graphics Technology Conference (GTC). This builds real-time awareness of the company's latest product innovations and drives fan following.
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