Lanxin Computing, headquartered in Shenzhen, has released a so-called supernode for convergent computing. This product expands the RISC-V AI inference platform from a single chip and server level to a cluster system.
The product unveiling took place at the RISC-V Summit in China in 2026, where CTO Jia Xinxia delivered the keynote speech on the LX500 processor that underpins this system.
The core design concept aims to minimize data movement. In a standard inference server, the central processor and a separate accelerator card have their own memory, and model weights, activations, and context data are transferred between them via the PCIe bus. Lanxin's approach integrates general-purpose computing and AI computing onto a single die, allowing both to utilize a shared memory pool. The company claims this improves time-to-first-token, pre-processing, and long-context handling metrics.
According to the company, the LX500 is equipped with 48 RISC-V cores, divided into 32 high-performance and 16 energy-efficient cores. It also includes AI vector and matrix engines capable of performing 75 TOPS of INT8 operations. The chip supports precisions from INT4 to FP32, uses shared DDR5 memory, and supports CXL memory expansion. Previous company materials indicated that the chip conforms to the RVA23 profile and RVV 1.0 vector extension.
The supernode completes the product line that Lanxin has been building over the past year. The LX500 processor was launched in February, publicly demonstrated in April, and used in a universal prototype in May. In July, Lenovo's WR5219 G5 server model built on this chip was introduced, and in August, the company began supplying LX500 samples to industrial customers, some of whom have started small-batch testing.
The current product assortment ranges from the chip and server up to the universal LX5000 device and the new supernode. However, Lanxin has not disclosed the number of chips in the supernode or the types of interconnects, leaving the system's scale undefined.
Regarding software, the company reported adapting Qwen, DeepSeek, and Kimi models for various parameter sizes, precisions, and long-context settings. Work is currently underway on a unified hardware and software stack to simplify the deployment process. Target applications include private enterprise knowledge bases with augmented retrieval, office agents, financial compliance analysis, offline question answering in government agencies, and on-site industrial inspection where local processing and data security are critical.
The release of this product adds a system product to the growing collection of Chinese RISC-V developments focused on data centers, alongside recent announcements of server CPUs and core intellectual property from other domestic developers. Whether the approach will be scalable depends on the performance evolution of the chips, the support of the software ecosystem, and real-world adoption.
