# Chaodongli

Founded in 2023/4, the company develops compact supercomputing AI servers for AIGC and metaverse inference, emphasizing low power, value and scalability. Its supercomputing architecture and integrated computing/networking form parallel clusters over lossless Ethernet for large-model inference. Claims seventy percent lower construction costs and eighty percent lower operating power than traditional AI servers. Has completed principal validation, prototypes and chip selection, with scaled delivery possible in six months.
Technology and architecture, claimed to lead by a year:
1. Servers contain only SOC computing units and a lossless-Ethernet switching backplane. Instead of PCIE, units communicate through high-speed lossless Ethernet using the COCE protocol.
2. Frontier technologies:
1) Parallel computing maps partitioned model logic to SOC networking and hardware, coupling collective computation and communication.
2) Embedded AI uses quantization and memory optimization to run partitioned models efficiently on SOCs.
3) Integrated computing/networking reduces expensive-unit waiting time, with claimed utilization above 75%.
4) Lossless Ethernet enables packet-loss-free, low-latency, nonblocking communication among model-computing nodes.
Market advantages:
1. At equal computing output, claims power use of 12% and price of 30% of mainstream AI servers. A fully configured 2U server consumes 300W.
2. Scalable: fewer than sixteen chips in a server, costing roughly a hundred thousand yuan or more, can serve model inference. Plug-in expansion within servers and high-speed data-center switches extend capacity across servers.
3. With 16 computing units, a server can run inference for a 170b model; full configuration delivers 2.5P INT8 inference. A switched cluster exceeding thirty thousand servers could run brain-scale models, claimed to exceed the world’s strongest supercomputer.
4. SOC GPU, CPU and NPU units can be scheduled flexibly for diverse workloads, business expansion and algorithm compatibility.
Supply chain: chips are described as resistant to supply restrictions and replaceable with domestic alternatives for local technology ecosystems.
Team: former senior Huawei technology, R&D, management and marketing experts with over 20 years of ICT and AI experience. Combines technology and market strengths, complementary leadership, growth management and contacts in AI and telecom. CTO and R&D VP have deep technical and management backgrounds; the CTO holds AI and distributed-computing patents and has published an AI book.
Market: extensive customers and promotional partners support rapid scaled sales, with a pipeline of representative industry clients.

Founded in 2023/4, the company develops compact supercomputing AI servers for AIGC and metaverse inference, emphasizing low power, value and scalability. Its supercomputing architecture and integrated computing/networking form parallel clusters over lossless Ethernet for large-model inference. Claims seventy percent lower construction costs and eighty percent lower operating power than traditional AI servers. Has completed principal validation, prototypes and chip selection, with scaled delivery possible in six months.
Technology and architecture, claimed to lead by a year:
1. Servers contain only SOC computing units and a lossless-Ethernet switching backplane. Instead of PCIE, units communicate through high-speed lossless Ethernet using the COCE protocol.
2. Frontier technologies:
1) Parallel computing maps partitioned model logic to SOC networking and hardware, coupling collective computation and communication.
2) Embedded AI uses quantization and memory optimization to run partitioned models efficiently on SOCs.
3) Integrated computing/networking reduces expensive-unit waiting time, with claimed utilization above 75%.
4) Lossless Ethernet enables packet-loss-free, low-latency, nonblocking communication among model-computing nodes.
Market advantages:
1. At equal computing output, claims power use of 12% and price of 30% of mainstream AI servers. A fully configured 2U server consumes 300W.
2. Scalable: fewer than sixteen chips in a server, costing roughly a hundred thousand yuan or more, can serve model inference. Plug-in expansion within servers and high-speed data-center switches extend capacity across servers.
3. With 16 computing units, a server can run inference for a 170b model; full configuration delivers 2.5P INT8 inference. A switched cluster exceeding thirty thousand servers could run brain-scale models, claimed to exceed the world’s strongest supercomputer.
4. SOC GPU, CPU and NPU units can be scheduled flexibly for diverse workloads, business expansion and algorithm compatibility.
Supply chain: chips are described as resistant to supply restrictions and replaceable with domestic alternatives for local technology ecosystems.
Team: former senior Huawei technology, R&D, management and marketing experts with over 20 years of ICT and AI experience. Combines technology and market strengths, complementary leadership, growth management and contacts in AI and telecom. CTO and R&D VP have deep technical and management backgrounds; the CTO holds AI and distributed-computing patents and has published an AI book.
Market: extensive customers and promotional partners support rapid scaled sales, with a pipeline of representative industry clients.

Canonical: https://uniquebase.ai/products/chaodongli
Stable identifier: UB-D530
Snapshot captured: 2026-10-15T14:23:27.067Z
English updated: 2026-10-16T01:35:01.088Z

## Connected profiles

- [Chaodongli](/companies/chaodongli)

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