打造世界级 AI 基础设施 成本降低 60%，利用率提升 3 倍
为 AI 平台公司和大型企业提供完整的 GPU 虚拟化和池化解决方案。超越僵化的厂商方案，提供高级调度、多厂商支持和企业级运维能力。
满足您的所有需求
为智能计算平台提供完整的 GPU 虚拟化和池化解决方案
企业级虚拟化
异构 GPU 虚拟化引擎，支持多种厂商和高级调度模式，实现最大灵活性。
完全掌控
基于 K8S 的企业级调度器，具有动态扩缩容、vGPU 切分和 Bin-packing 策略，实现完整的资源控制。
创新驱动
GPU-over-IP 远程算力调用引擎，采用低延迟技术和标准化管理 API。
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Core Features
Heterogeneous GPU Virtualization Engine
Support for various virtualization modes from heterogeneous vendors, advanced scheduling modes, virtual machine vGPU solutions.
GPU-over-IP Remote Computing Power Calling Engine
Low-latency remote GPU invocation technology for accessing GPU resources across networks.
K8S-based Enterprise-grade Computing Power Scheduler
Dynamic scaling, vGPU partitioning, bin-packing, rebalance, and other advanced scheduling strategies.
K8S CRD-based Standardized System Management API
Standardized APIs for consistent operations across environments and vendors.
Standard Monitoring and Alerting Components
Comprehensive monitoring, metrics, and alerting for heterogeneous AI infrastructure.
Target Customers
Intelligent Computing Platform R&D Enterprises
Companies building AI infrastructure platforms that need complete GPU virtualization and pooling solutions.
Large Enterprise AI Infra Teams
Enterprise teams managing GPU infrastructure who need advanced scheduling and virtualization capabilities.
Neo Cloud (Emerging GPU Cloud Vendors)
New GPU cloud vendors looking for flexible, scalable GPU management solutions.
Key Benefits
Flexible GPU Partitioning
GPU devices can be flexibly partitioned according to user computing power requirements with complete virtualization and isolation.
Advanced Scheduling
Move beyond rigid OEM and default K8s solutions. Achieve dynamic scaling, vGPU partitioning, bin-packing, and rebalance.
Simplified Operations
Standardized components reduce O&M complexity. Unified monitoring, alerting, and billing modules eliminate redundant development.
Standardization and Automation
High standardization and automation levels reduce maintenance burden for intelligent computing platform providers.
Pricing Model
One-time buyout + maintenance fee, or joint R&D for large projects
One-time Buyout + Maintenance Fee
After platform first deployment, can be used indefinitely in other customer projects. Each customer pays a small fixed annual maintenance fee for subsequent maintenance and upgrades.
Joint R&D
For large projects, priced individually based on the proportion of investment from both parties.
Transform Your GPU Infrastructure
Contact us to learn how Tensor Fusion Engine can help your organization