> For the complete documentation index, see [llms.txt](https://gpu-ai.gitbook.io/gpu-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gpu-ai.gitbook.io/gpu-ai/gpu-ai-architecture.md).

# GPU AI Architecture

<figure><img src="/files/tyH4XAhngp3fOWmK2FNq" alt=""><figcaption></figcaption></figure>

The GPU AI Portal's architecture is a multi-layered, cohesive structure that provides a seamless, secure, and efficient user experience. Each layer has a distinct role, working in tandem to ensure the system's optimal performance. The architecture is built upon modern technologies, ensuring scalability, reliability, and robustness.

#### User Interface

This layer is the visual gateway for users. It comprises the Public website, Customers area, and GPU providers area (Workers). The design is intuitive and user-centric, ensuring easy navigation and interaction.

> **Mainly used Tech Stack:** ReactJS, Tailwind, web3.js, zustand.

#### Security Layer

A pivotal layer ensuring the system's integrity and safety. It encompasses a Firewall for network protection, an Authentication Service for user validation, and a Logging Service for tracking activities.

> **Mainly used Tech Stack:**&#x46;irewall (pfSense, iptables), Authentication (OAuth, JWT), Logging Service (ELK Stack, Graylog).

#### API Layer

Serving as the communication bridge, this layer has multiple facets: Public API for the website, Private APIs for Workers/GPU Providers and Customers, and Internal APIs for Cluster Management, Analytics, and Monitoring/Reporting.

> **Mainly used Tech Stack:** FastAPI, Python, GraphQL, RESTful services, gunicorn, solana.

#### Backend Layer

The system's powerhouse. It manages Providers (Workers), Cluster/GPU operations, Customer interactions, Fault Monitoring, Analytics, Billing/Usage Monitoring, and Autoscaling.\
**Mainly used Tech Stack:** FastAPI, Python, Node.js, Flask, solana, IO-SDK (a fork of Ray 2.3.0), Pandas.

#### Database Layer

The data repository of the system. It uses Main storage for structured data and Caching for temporary, frequently accessed data.

> **Mainly used Tech Stack:** Postgres (Main storage), Redis (Caching).

#### Message Broker/Task Layer

This layer orchestrates asynchronous communications and task management, ensuring smooth data flow and efficient task execution.

> **Mainly used Tech Stack:** RabbitMQ (Message Broker), Celery (Task Management).

#### Infrastructure Layer

The foundational layer. It houses the GPU Pool with hardware from our verified partners. Orchestration tools manage deployments, while Execution/ML Tasks handle computations and machine learning operations. Additionally, it provides Data Storage solutions. GPU performance is monitored using Nvidia-smi or NVIDIA DCGM.

> **Mainly used Tech Stack:**

* GPU/CPU Pool
* Orchestration: Kubernetes, Prefect, Apache Airflow
* Execution/ML Tasks: Ray, Ludwig, Pytorch, Keras, TensorFlow, Pandas
* Data Storage: Amazon S3, Hadoop HDFS
* Сontainerization: Docker
* Monitoring: Grafana, Datadog, Prometheus, NVIDIA DCGM


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