Scalable, secure, and high-performance infrastructure for seamless AI deployment.
FinBlade AI is built on a high-performance hardware infrastructure designed to support secure, scalable, and efficient AI workloads. Our architecture integrates a front-end server for seamless user interactions, a backend server for data processing, and a locally deployed Large Language Model (LLM) running on BareMetal servers to ensure optimal security and performance. With an optimized GPU infrastructure, FinBlade AI enables accelerated AI processing, dynamic scalability, and intelligent data management, allowing organizations to leverage AI-driven insights, automated alerts, and customized reports for enhanced operational efficiency.
FinBlade AI offers three distinct deployment models to cater to diverse business needs:
For organizations requiring the highest levels of security and control, on-premises deployment enables the hosting of FinBlade AI within the client’s physical data center. This approach ensures full ownership of data and infrastructure, ideal for businesses prioritizing compliance, governance, and customization.
Organizations seeking a balance between security and scalability can opt for private cloud deployment. FinBlade AI supports isolated, containerized environments hosted on local (KSA-Based) or global cloud providers, ensuring robust data privacy while maintaining flexible resource allocation.
For businesses prioritizing rapid deployment and minimal operational overhead, FinBlade AI’s SaaS model provides a fully managed cloud platform. This option ensures streamlined onboarding, automatic updates, and continuous maintenance, enabling organizations to adopt AI capabilities with ease.
To run FinBlade AI on-premises, organizations require a tailored hardware setup optimized for their AI workloads. The choice of AI model depends on specific business requirements and use cases. Larger models offer deeper contextual understanding and improved performance in specialized domains such as legal and healthcare.
| Model Size | Description |
|---|---|
| 8 Billion Parameters | Handles a broad range of general language tasks but may have limitations in deep contextual understanding. |
| 70 Billion Parameters | Captures nuanced language and performs better in context-heavy and creative tasks. |
| 405 Billion Parameters | Provides sophisticated, deep-context comprehension across complex subjects, delivering highly coherent outputs. |
FinBlade AI incorporates multiple layers of security to address corporate AI adoption challenges. Our security framework includes:
With a foundation of cutting-edge hardware and advanced security protocols, FinBlade AI delivers a powerful, scalable, and secure AI environment for enterprises. Whether deployed on-premises, in a private cloud, or as a SaaS solution, FinBlade AI ensures that businesses can leverage AI-driven insights while maintaining full control over their data and infrastructure.
For more details on FinBlade AI’s hardware requirements and deployment options, contact our team today.
The following configuration provides recommended hardware requirements for production-grade
| System Requirements | ||
|---|---|---|
| System Component | Hardware Specs (70B Models) | Hardware Specs (405B Models) |
| Al Server |
Nvidia HGX-H100 or Liqid Chassis with 8 x RTX GPUs to run 70B Al models |
2 x Nvidia HGX-H200 or Liqid Chassis with 32 x RTX GPUs to run 405B Al models |
| Front-End server |
1TB SSD, 16 Core CPU, 64 GB RAM 10 GB Network Card Linux Ubuntu with docker |
|
| Middleware Server |
1TB SSD ,18 Core CPU, 128 GB RAM Single GPU with 24GB VRAM (Nvidia A10 or higher) 10 GB Network Adaptor Linux Ubuntu with docker |
|
| File Storage | Sized based on Client's requirement | |
| Vector Storage | Depends on File Storage size | |
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For technical support and assistance, please contact us at support@finblade.ai. Our support team is available 24/7 to address any technical issues or questions you may have.
© 2025 FinBladeAi. All Right Reserved