—————Table of Contents—————–1. Product Release Date2. Supported GPU Models3. Implementation Methods4. Technical Advantages and Disadvantages5. Core Functional Features6. Application Scenarios7. Deployment Forms8. Customer Cases9. Maintenance Support and Community Activity
10. Pricing and Costs
11. User Perspective Summary: Why Choose OrionX?
12. Comparison Recommendations
13. Future Outlook
—————Content—————
1. Product Release Date
| Product | Release Date | User Perspective Interpretation |
|---|---|---|
| OrionX | 2019 | As one of the earliest pioneers in the GPU resource pooling field, OrionX has undergone multiple rounds of technological iterations (such as the terminology definition in 2022), leading in technological maturity. |
| HAMi | 2023 | An emerging product based on the Kubernetes ecosystem, suitable for cloud-native scenarios, but lacks the years of industry validation that OrionX has. |
| GPUStack | 2024 | New product, has not yet formed large-scale user cases, and its technical stability needs to be verified. |
| cGPU/qGPU | 2020 | NVIDIA’s official technology (such as cGPU) already exists, but its functionality is limited (such as static resource allocation), making it difficult to meet dynamic demands. |
2. Supported GPU Models
| Product | Supported Models | User Perspective Interpretation |
|---|---|---|
| OrionX | NVIDIA, AMD, Cambricon, Huawei Ascend, Haiguang DCU, Moore Threads, etc. (covering domestic/international brands). | Emphasizes multi-brand heterogeneous computing compatibility, especially suitable for mixed computing environments (such as domestic replacement scenarios). |
| HAMi | NVIDIA, Huawei Ascend, Haiguang DCU, Cambricon (focusing on domestic adaptation). | Strong domestic adaptation capability, but weak support for international brands (such as AMD), with obvious limitations. |
| GPUStack | Only supports NVIDIA GPUs (such as A100, H100). | Relies on the NVIDIA ecosystem, unable to meet multi-brand heterogeneous needs, lacking flexibility. |
| cGPU/qGPU | Only supports NVIDIA GPUs (such as Tesla, T4 series). | Functionality is limited to NVIDIA hardware, unable to respond to domestic trends or mixed computing scenarios. |
3. Implementation Methods
| Product | Core Technology | User Perspective Interpretation |
|---|---|---|
| OrionX | GPU over IP/IB + vGPU slicing + RDMA network communication (supports 1% computing power/1MB video memory granularity). | Achieves ultra-low latency (<2% performance loss) through user-mode pooling technology, and supports cross-network resource pooling, suitable for large-scale data centers. |
| HAMi | Kubernetes scheduler plugin + container-level resource isolation (cgroup/namespaces). | Relies on the Kubernetes ecosystem, but scheduling flexibility is limited, with higher performance loss (5-10%). |
| GPUStack | containerized GPU sharing + static resource allocation. | Simple functionality, lacking dynamic scheduling capability, with low resource utilization. |
| cGPU/qGPU | NVIDIA official virtualization technology (such as MIG, vGPU). | Static resource allocation, unable to adjust dynamically, and relies on NVIDIA hardware, lacking flexibility. |
4. Technical Advantages and Disadvantages
| Dimension | OrionX | HAMi | GPUStack | cGPU/qGPU |
|---|---|---|---|---|
| Performance Loss | Remote vGPU loss <2% (depends on RDMA), local vGPU has almost no loss. | Containerized deployment loss 5-10%, affected by Kubernetes scheduling overhead. | Relies on containerization, with higher performance loss. | Static allocation, low performance loss, but poor flexibility. |
| Resource Utilization | Ultra-fine granularity slicing (1% computing power/1MB video memory), utilization increased by 3-10 times. | Medium granularity (core-level/video memory ratio), utilization increased by 2-5 times. | Static allocation, low utilization. | Static allocation, low utilization (e.g., MIG requires pre-allocation). |
| Deployment Complexity | Requires dedicated networks (IB/RoCE) or standard TCP/IP networks, complex deployment but stable performance. | Relies on Kubernetes clusters, simple deployment but requires maintenance of cloud-native ecology. | ||
| Domestic Adaptation | Supports domestic chips such as Huawei Ascend, Haiguang DCU, Cambricon, suitable for domestic replacement needs. | Designed specifically for domestic adaptation, but weak support for international brands. | ||
| Scalability | Supports horizontal scaling, easily managing the entire data center’s GPU resources. | Depends on the scale of the Kubernetes cluster, scalability is limited. | Poor scalability, suitable for small-scale scenarios. | Poor scalability, depends on the number of physical GPUs. |
5. Core Functional Features
| Product | Core Features | User Perspective Interpretation |
|---|---|---|
| OrionX | – Hot Migration (migrating GPU without interrupting tasks)– Resource Over-allocation– Dynamic Resource Allocation– Remote Resource Invocation | Provides enterprise-level operation and maintenance capabilities, such as hot migration to actively optimize load, resource quotas to ensure fair distribution, suitable for high-demand scenarios like finance and securities. |
| HAMi | – Kubernetes Scheduler Plugin– Priority Preemption– Container-level Isolation | Suitable for cloud-native scenarios, but lacks advanced operation and maintenance features (such as hot migration) compared to OrionX. |
| GPUStack | – GPU Sharing within Containers– Static Resource Allocation | Basic functionality, unable to meet dynamic demands (such as elastic resource allocation for AI training/inference). |
| cGPU/qGPU | – MIG (Multi-Instance GPU)– vGPU Virtualization | Functionality is limited, unable to dynamically adjust resources, and only supports NVIDIA hardware. |
6. Application Scenarios
| Product | Typical Scenarios | User Perspective Interpretation |
|---|---|---|
| OrionX | – AI Training/Inference– Financial Risk Control– Computing Power Center– Mixed Computing Environment (Domestic + International) | Suitable for enterprise-level data centers and mixed computing scenarios, such as deployments in the production environments of Everbright Bank and Ping An Securities. |
| HAMi | – Cloud-native AI Tasks– Microservice Containerization– Multi-tenant Resource Sharing | Suitable for public/private cloud scenarios, but weak support for scenarios with high real-time requirements (such as HPC). |
| GPUStack | – Small-scale AI Development– Educational and Research Scenarios | Simple functionality, suitable for non-production environments or experimental scenarios. |
| cGPU/qGPU | – Enterprise-level AI Inference– Static Task Allocation | Suitable for scenarios with high stability requirements but low flexibility needs (such as edge computing). |
7. Deployment Forms
| Product | Deployment Model | User Perspective Interpretation |
|---|---|---|
| OrionX | Fully Distributed Deployment + Private Deployment (K8S/Containers/VMs/Physical Machines). | Suitable for data center-level private deployment, such as core trading systems of securities companies. |
| HAMi | Kubernetes Cluster Deployment (Helm Chart). | Suitable for cloud-native environments, but limited support for traditional data center deployments. |
| GPUStack | Containerized Deployment (Docker/Kubernetes). | Suitable for lightweight container scenarios, but lacks enterprise-level private deployment capabilities. |
| cGPU/qGPU | NVIDIA Driver + Hardware Deployment (requires physical GPUs). | Relies on NVIDIA hardware, high deployment threshold, suitable for enterprises already in the NVIDIA ecosystem. |
8. Customer Cases
| Product | Typical Customers | User Perspective Interpretation |
|---|---|---|
| OrionX | Everbright Bank (AI computing power pooling), Ping An Securities (financial risk control), three major telecom operators (data center GPU management). | Proves OrionX’s mature applications in finance, securities, telecom operators, and enterprise-level data centers, with high reliability and scalability. |
| HAMi | iFlytek (Xingchen MaaS platform), Huawei (Ascend heterogeneous scheduling), Tencent Cloud (rendering GPU sharing). | Suitable for domestic replacement and cloud-native scenarios, but lacks deep validation in industries like finance and manufacturing. |
| GPUStack | Educational institutions, startups (small-scale AI development). | Suitable for experimental scenarios, but lacks large-scale production environment cases. |
| cGPU/qGPU | Enterprise-level AI inference (such as autonomous driving, edge computing). | Suitable for NVIDIA hardware users, but cannot replace OrionX’s multi-brand compatibility. |
9. Maintenance Support and Community Activity
| Product | Maintenance Support | Community Activity |
|---|---|---|
| OrionX | Provides enterprise-level technical support (phone/email/on-site), regular version updates (every two weeks). | Low community activity, relies on internal team maintenance, with few open-source contributions. |
| HAMi | Maintained by Huawei Cloud, with high community activity, but relies on the Kubernetes ecosystem. | High community activity, but mainly focuses on cloud-native scenarios, lacking in-depth technical discussions. |
| GPUStack | Limited community support, mainly relies on individual developer maintenance. | Low community activity, with few technical documents and cases. |
| cGPU/qGPU | Maintained by NVIDIA, with frequent updates, but limited to NVIDIA hardware. | High community activity, but only around NVIDIA hardware, lacking heterogeneous support. |
10. Pricing and Costs
| Product | Pricing Model | User Perspective Interpretation |
|---|---|---|
| OrionX | Enterprise-level licensing fees (charged per node or GPU count), with high long-term ROI (resource utilization increased by 3-10 times). | Higher initial investment, but long-term savings on computing power costs, suitable for enterprises with sufficient budgets. |
| HAMi | Free and open-source (led by Huawei Cloud), but requires costs associated with the Kubernetes ecosystem. | Suitable for budget-limited cloud-native scenarios, but lacks enterprise-level features. |
| GPUStack | Open-source and free, but with limited functionality, requiring additional development costs. | Suitable for experimental scenarios, but cannot meet production needs. |
| cGPU/qGPU | NVIDIA official licensing fees (charged per GPU count), requires purchase of NVIDIA hardware. | High costs (hardware + licensing), suitable for enterprises already in the NVIDIA ecosystem. |
11. User Perspective Summary: Why Choose OrionX?
| Dimension | OrionX Advantages |
|---|---|
| Technological Leadership | First to propose the concept of GPU pooling, supports multi-brand heterogeneous computing, leading in technological maturity. |
| Performance and Efficiency | Ultra-fine granularity slicing (1% computing power/1MB video memory) + hot migration technology, resource utilization increased by 3-10 times, with extremely low performance loss. |
| Domestic Adaptation | Supports domestic chips such as Huawei Ascend, Cambricon, Haiguang, meeting domestic replacement needs. |
| Enterprise-level Capabilities | Provides advanced operation and maintenance features such as hot migration, resource quotas, multi-tenant isolation, and multi-resource groups, suitable for high-demand scenarios like finance and securities. |
| Long-term Value | Over three years of industry validation (such as Everbright Bank, Ping An Securities), reducing enterprise trial and error risks. |
12. Comparison Recommendations
- Choose OrionX: If your enterprise needs multi-brand heterogeneous computing pooling, ultra-low performance loss, and enterprise-level operation and maintenance capabilities (such as hot migration, resource quotas), and the deployment environment is data center or mixed computing scenarios, OrionX is the best choice.
- Choose HAMi: If your business focuses on cloud-native containerization and requires rapid deployment, but has low requirements for domestic adaptation, consider HAMi.
- Choose GPUStack/cGPU/qGPU: If your needs are concentrated on NVIDIA hardware ecosystem or small-scale experimental scenarios, these products may be more economical, but cannot meet OrionX’s dynamic scheduling and domestic adaptation capabilities.
13. Future Outlook
As the definers of GPU pooling, OrionX will continue to promote standardization of heterogeneous computing and intelligent resource pooling. With the popularization of domestic chips (such as Ascend, Haiguang, Cambricon), OrionX’s multi-brand compatibility will become a core competitive advantage for enterprises’ digital transformation. Other products (such as HAMi, GPUStack) need to break through in resource pooling,performance loss and domestic adaptation to compete with OrionX.