The NVIDIA Vera Rubin platform is NVIDIA's next-generation flagship computing platform after Blackwell. This article provides an in-depth analysis covering the naming origin, 6-chip packaging, memory subsystem, compute matrix, networking architecture, rack-scale solution, and software ecosystem.
On April 17, 2026, Cerebras Systems formally filed its S-1 prospectus with the SEC, applying to list on Nasdaq, targeting May 2026. This is one of the most significant IPOs in the AI chip industry in 2026. This article provides a deep analysis of Cerebras's finances, strategy, customers, and future.
In Q1 2026, one of the biggest pieces of news in the AI chip industry: NVIDIA acquired Groq for approximately $20 billion in a full acquisition. This means Groq's LPU architecture officially becomes part of NVIDIA's compute landscape, complementing GPUs. This article analyzes the strategic significance of this acquisition in detail.
On December 2, 2025, at the re:Invent 2025 conference, AWS formally GA'd its third-generation custom AI training chip Trainium 3. This is a critical upgrade to the AWS compute landscape: 3nm process, 4.4× compute improvement, 4× efficiency improvement, Trn3 UltraServer with 144 chips. This article provides a detailed analysis.
Huawei Ascend 920 (昇腾 920) entered large-scale mass production in 2025 H2, representing a major breakthrough for Chinese domestic AI chips. This article analyzes its specifications, comparison with NVIDIA H20, the CloudMatrix 384 Ultra system, and its significance for China's AI industry.
Escalating U.S. export controls are forcing China's AI chip industry to accelerate self-reliance. By 2025, the discussion around domestic Chinese AI chips has shifted from "are they usable?" to "which one should I choose?"
This article systematically reviews the major players, core products, and actual deployment status of domestic AI chips, helping developers and procurement decision-makers understand the competitive landscape.
The AI accelerator chip space has three major mainstream architectures: GPU, NPU, and TPU. Add the recently emerging LPU (Language Processing Unit), and many developers find it hard to tell them apart.
This article compares them across four dimensions: architectural design philosophy, ecosystem maturity, real-world performance, and deployment cost.
In 2025, the AI accelerator market has become unprecedentedly rich. From NVIDIA's Blackwell to Huawei Ascend 910B, from Google TPU v6 to Groq LPU, developers face more choices than ever before.
But this is both a blessing and a challenge — picking the wrong card means either wasting money or falling short on performance.
This article helps you sort out the selection logic starting from actual workloads.
What's new in this update:
MirrorFrog is officially live! An open-source AI accelerator driver and documentation directory.
Currently cataloged content includes: