Adaptive Computing AI Server

Background

• Training large AI models requires massive compute clusters, but traditional high-end GPU systems are prohibitively expensive.

• SMEs struggle to enter the AI R&D race due to limited compute and funding.

• Existing systems have poor scalability, relying heavily on premium GPUs such as NVIDIA H100, creating supply chain risks.

About the Project (Solution)

• Distributed Computing Platform

  • Adaptive mesh topology + compute-network fusion enabling linear scalability to millions of nodes.

  • Supports both mainstream and consumer-grade GPUs, significantly reducing hardware dependency and procurement cost.

  • Natively supports cutting-edge architectures like MoE (Mixture of Experts), fully compatible with mainstream AI toolchains.

Project Advantages

• Cost Efficiency: 3–5X lower training costs, 40%+ TCO reduction.

• Unlimited Scalability: From lab setups to million-node clusters, compute grows linearly on demand.

• Supply Chain Resilience: Works with multiple GPU types, no reliance on a single vendor.

• Ecosystem Compatibility: Plug-and-play with CUDA and mainstream open-source LLMs, no extra coding required.

• Operational Friendly: Self-healing reliability + energy-efficient design, no liquid cooling needed.

Application scenarios

• Large Language Model (LLM) training and inference.

• Multi-modal AI (speech, vision, NLP) development.

• Industry-specific AI applications in finance, healthcare, education.

• Enterprise & government-grade sovereign AI infrastructure.

Team & Background

• Founded in 2019, headquartered in Beijing & Shenzhen.

• 20+ core patents, backed by 20 years of expertise in high-performance networking.

• Founding team with proven track record in commercialization and industry-grade product development.

• Mission: Democratize AI compute and reshape the digital ecosystem.