In today's rapid development of artificial intelligence technology, the improvement of arithmetic power and the optimization of energy efficiency have become the key to AI chip design. According to analyst Ming-Chi Kuo, NVIDIA's R100 AI chip will begin mass production in the fourth quarter of 2025. The chip will use TSMC's N3 process technology, which is expected to deliver higher performance and energy efficiency ratios compared to the N4P process used in its predecessor, the B100.The R100 is designed with an approximately 4x reticle design, which means that it is likely to see a significant increase in performance. Meanwhile, the R100 will utilize CoWoS-L packaging technology, in line with the B100. In terms of design, the R100 utilizes a ~4x reticle design, compared to the B100's 3.3x, which could mean an increase in chip area and integration for the R100. In addition, the R100 is expected to be paired with eight HBM4 memories to support its high-performance computing needs.
With increasingly stringent energy efficiency requirements in data centers, NVIDIA has paid special attention to power consumption optimization in the design of the R100. The previous-generation chip, GB200, has already achieved a 25-fold reduction in energy consumption compared to H100, an improvement that shows that NVIDIA is actively responding to the market's concerns about energy efficiency while improving arithmetic power. As a result, the R-series chips and system solutions have been designed with a special focus on optimizing energy consumption in addition to boosting AI arithmetic power. This has been reflected in the design of the previous generation of chips, for example, the GB200 reduces cost and energy consumption by 25 times compared to the H100.
The R100 is expected to be paired with eight HBM4s, a high-performance memory technology that will further boost the chip's processing power. Meanwhile, the size of R100's intermediary layer (Interposer) has not yet been finalized, demonstrating NVIDIA's flexibility and innovation in packaging technology.
In addition to hardware breakthroughs, NVIDIA has also made new progress in the field of AI software. the launch of NIM microservices will help developers use NVIDIA's inference software to quickly build AI applications. In addition, AI software programs such as NVIDIA's simulation platform, Omniverse, and the Isaac Robotics platform continue to push the boundaries of AI technology.
The mass production and launch of the R100 will further drive the development and application of AI technology, especially in the data center, smart computing and automation fields. NVIDIA's continuous innovation not only brings strong hardware support to the AI industry, but also provides developers with a wealth of software tools and platforms, which together promote the prosperity of the AI ecosystem.
