Industry experience
Compute and AI applications
Elephant AI is the AI infrastructure and enterprise applications brand of Shenzhen Jisu Xingkong Technology Co., Ltd.
Shenzhen Jisu Xingkong Technology Co., Ltd. operates the Elephant AI brand across nationwide clusters, hardware and enterprise AI. Qunxiang Cloud connects chip owners to compute demand, organizing clusters, hosting, delivery and business-income settlement.
Nationwide compute and data-center partnerships support inference, content generation, rendering and research for enterprises, developers and AI application teams. Planned compute-platform and community-incubation services extend the business model.
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Compute and AI applications
Enterprises and development teams
Connecting business needs
Connect terminals, servers and cloud resources for enterprise and local deployment.
Clients own their chips while Qunxiang Cloud operates them. Business income is settled as agreed, with continued hosting or buyback under the applicable documents.
Token Plan, agent services and machine rental are being prepared for model usage, intelligent collaboration and equipment rental. Features and availability will be announced later.
From GPU nodes to verified business results.
Power, cooling and GPU boards
Plan GPU boards, power, cooling and PCIe. Verify each node.
Schedule workloads, isolate resources and recover failed jobs.
Retrieve knowledge, call tools and check task outputs.
Service records make each step verifiable. Documenting the equipment, environment, capacity and results helps customers clarify what they are buying, how it will be delivered and how it will be accepted.
Record each GPU’s model, serial number, ownership and host node. Distinguish equipment ownership from service usage and participation in operations.
Record model versions, weight hashes, images, drivers and inference parameters so tests, upgrades and rollbacks can be reproduced.
Admission rules account for tenant permissions, model readiness, reservations and node health. A pool’s total GPU memory can exceed what a single task can use.
Check input specifications, accepted results, total cost across attempts and exception records. Use temperatures, power draw and maintenance tickets to guide recovery.
Equipment, the software ecosystem and enterprise requirements shape the same delivery process. Engineering value is measured by reliability, accepted outputs and total cost under the same service requirements.
GPU boards, hosts, power, cooling and networking form a service node. Compatibility depends on the actual configuration.
The technical design uses common frameworks such as Kubernetes, NVIDIA GPU Operator, Kueue and vLLM to organize device management and job execution.
Define specifications, service periods and deliverables for dedicated capacity, application integration and batch media workflows.
Track accepted paid deliveries, recovery and total cost to assess engineering value and build records of model performance and service.
Discuss nationwide compute, chip hosting, hardware, enterprise AI or hardware recycling.