In 2026, the AI chip market is undergoing a profound architectural transformation from "training first" to "inference is king". As large models move from the laboratory to large-scale deployment, the demand for inference computing is growing exponentially, driving a fundamental shift in chip design paradigms.
In the past five years, the competition for AI chips has almost entirely focused on training performance. NVIDIA dominates the training market with its CUDA ecosystem and Hopper/Blackwell architecture, increasing single card computing power from 312 TFLOPS in 2022 to over 2000 TFLOPS in 2026. However, as we enter 2026, a significant trend change is occurring - the market size of inference computing exceeds that of training computing for the first time, accounting for over 55% of the total AI computing power demand.
This transformation has spurred the flourishing development of inference specific chips. Groq's LPU (Language Processing Unit) adopts a unique tensor flow architecture to achieve millisecond level response in large language model inference scenarios. Compared with general-purpose GPUs, LPU reduces latency by 5 times and energy consumption by 3 times in LLM inference. Groq has obtained bulk purchase orders from multiple cloud service providers in early 2026 and deployed them in latency sensitive application scenarios such as online customer service and real-time translation.
Domestic AI chip manufacturers are also accelerating their layout in the field of inference. The Huawei Ascend 910C has improved inference performance by 80% compared to the previous generation, and its CANN software stack has been adapted to over 200 mainstream AI models. The Cambrian Siyuan 600 series focuses on edge inference scenarios, achieving low-power and high-efficiency deployment in smart cities, industrial quality inspection, and other scenarios. According to data from the first quarter of 2026, the share of domestically produced AI chips in the inference market has increased from 22% in 2025 to 35%.
More noteworthy is the breakthrough in the integrated storage and computing architecture. In the traditional von Neumann architecture, the transfer of data between storage and computing units consumes a significant amount of time and energy, known as the "storage wall" bottleneck. In 2026, several start-up companies launched prototype products of integrated storage and computing AI chips, which directly embed computing units into storage arrays. The storage computing integrated chip represented by Zhicun Technology WT2000 has an energy efficiency ratio of more than 10 times that of traditional GPUs in lightweight inference tasks such as speech recognition and keyword wake-up.
At the same time, the interconnection architecture of AI chips is also undergoing innovation. As the scale of model parameters exceeds the trillion level, single card inference can no longer meet the demand, and high-speed interconnection between chips has become a key bottleneck. In 2026, NVIDIA's NVLink 6 interconnect bandwidth will reach 4.5TB/s and support seamless collaborative inference with 256 GPUs. AMD's Infinity Fabric and Huawei's HCCS interconnect solutions have also achieved ultra high speed chip to chip communication.
The competitive landscape of the AI chip market has also shifted from "one dominant player" to "a hundred flowers blooming". In addition to traditional giants such as NVIDIA, AMD, and Intel, over 50 startups have launched differentiated products in different niche areas. The cutting-edge directions of RISC-V architecture, such as AI accelerators, photon AI chips, and quantum neural network chips, have also achieved laboratory level breakthroughs in 2026.
━
This article is a comprehensive compilation of industry information and analysis reports that have been publicly released.