# Micro Cloud Service

GPGPU offers micro cloud services tailored for data processing and AI training in startups and AI projects. Micro cloud services are cloud products designed for users who prioritize extreme cost efficiency. Startups, small enterprises, and research institutions often value lower costs over faster turnaround times due to limited budgets. GPGPU integrates De-Fi protocols to deliver micro cloud services at a lower cost than other decentralized GPU cloud providers, enabling users to conduct testing, simple processing and computation, and ML/DL tasks for AI models.



### **Challenges in Decentralized GPU Clouds**

* **Low Availability**: Despite using idle GPUs, most GPUs remain in a 'ready' state within the protocol.
* **Simple Resource Allocation**: Limited selection options are available as categories are based solely on GPU chipset and network status.
* **Regional Imbalance**: GPUs are selectively hired from regions or countries with high client demand, leading to an imbalance between supply and demand.

These issues arise because the personal needs of GPU providers are not adequately addressed. GPU providers operate in diverse countries, regions, and network environments, with varying needs. Even among providers in the same region with similar products, their goals and preferences may differ. Below are examples of three user personas:



### Stably Converting Low-Quality GPUs

In a decentralized GPU cloud, it is possible to receive GPUs with low performance or irregular availability and cluster them to transform them into stable computing resources. The goal is to enhance cost efficiency, maximize the utilization of existing resources, and meet high-performance computing demands effectively.

**GPU Clustering**

* **Method**: Multiple GPUs are combined into a single logical unit to process tasks collaboratively.
* **Technologies**: Leverage frameworks such as CUDA, OpenCL, or Apache Spark to distribute data and workloads across GPUs.
* **Outcome**: Aggregates the computational power of individual GPUs to overcome their inherent limitations.

**Resource Stabilization**

* **Method**: Ensure the stability of the GPU cluster through fault recovery and efficient workload distribution.
* **Technologies**:
  * **Fault Tolerance**: Design redundancy to maintain system integrity even if one GPU fails.
  * **Task Orchestration**: Utilize tools like Kubernetes or Slurm to allocate tasks efficiently across the cluster.
  * **Load Balancing**: Distribute workloads evenly among GPUs to prevent bottlenecks.
* **Outcome**: Guarantees a reliable and resilient computing environment.

**Performance Optimization**

* **Method**: Enhance processing efficiency and resource utilization in a distributed environment.
* **Technologies**:
  * **Parallel Processing**: Split tasks across multiple GPUs to accelerate computation.
  * **Memory Optimization**: Optimize GPU memory usage to minimize performance degradation.
  * **Data Efficiency**: Improve data transfer efficiency in bandwidth-constrained distributed systems.
* **Outcome**: Delivers high-quality performance from low-quality GPU resources.

By clustering low-performance or irregularly available GPUs in a decentralized cloud and applying stabilization and optimization techniques, we can create a cost-effective, stable, and high-performing computing resource. This approach not only leverages existing hardware but also addresses the growing need for scalable computational power.



### **User Personas**

* **Jade** can supply GPUs consistently, except for specific periods. Although rental rewards may be irregular, she seeks higher rental income.
* **Katie** can also supply GPUs consistently but prefers predictable and stable rental income, even if it is slightly lower.
* **Henry** is in an environment where he can only provide GPUs very irregularly. He values using his GPUs himself but is interested in earning rental income, no matter how low, during idle times.



Thus, GPGPU must consider not only the GPU's chipset and network status but also accommodate the varied situations and needs of GPU providers. GPGPU offers solutions through three types of GPU pools:

* **Fixed Pool**: For high-performance products that can provide stable GPU availability and yield high rental income (Jade).
* **Bidding Pool**: For managing equipment rentals through direct bidding, suitable for those seeking irregular but high returns (Henry).
* **Flexible Pool**: Combined with De-Fi to provide stable and predictable rental income (Katie, Henry).

By addressing the diverse needs of GPU providers, GPGPU can offer more comprehensive and flexible solutions. Satisfying the needs of GPU providers directly translates to providing GPU users with a wider range of options and a more stable service.
