Building a High-Performance GPU Server for Large Language Models (LLMs) and On-Premises AI Development
Introduction
At Archsolution Limited, we are constantly pushing the boundaries of technological innovation to support businesses in harnessing the power of artificial intelligence. Our latest initiative involves collaborating with our sister company, Clear Data Science Limited (CDS), to build a state-of-the-art GPU server dedicated to fine-tuning Large Language Models (LLMs) and developing data-driven applications for the insurance industry.
This development represents a significant step forward in AI infrastructure, allowing businesses to leverage advanced machine learning models locally. By providing an on-premises AI environment, we offer enterprises an alternative to cloud-based AI solutions, ensuring enhanced security, cost efficiency, and full control over their data.
The GPU Server Build: High-Performance Hardware for AI
Building an AI-ready server requires powerful and scalable hardware capable of handling the massive computational demands of LLM training and inference. Our custom-built server is designed to support CDS in developing AI applications for insurance clients. The key specifications of our server include:
- Processor: AMD EPYC (Enterprise-Grade Performance)
- Memory: 512GB RAM (Ensuring smooth multi-tasking and large dataset handling)
- Graphics Processing Unit (GPU): 2 x NVIDIA RTX 3090 with NVLINK bridge
Why AMD EPYC?
The choice of the AMD EPYC processor was driven by its high core count, excellent multi-threading capabilities, and superior memory bandwidth. This makes it an ideal choice for deep learning workloads, ensuring fast and efficient model training.
Powering AI with NVIDIA RTX 3090 & NVLINK
One of the most critical components of the server is the dual NVIDIA RTX 3090 GPUs connected via an NVLINK bridge. This setup allows for increased memory pooling and improved GPU-to-GPU communication, significantly enhancing AI performance. The benefits of using NVLINK include:
- Memory Pooling: By combining the memory of two GPUs, the system can handle larger model sizes without out-of-memory errors.
- High Bandwidth: NVLINK provides a high-speed connection between GPUs, reducing communication latency.
- Improved Parallel Processing: Distributed training and inference are faster, allowing for better model optimization.
With this powerful hardware setup, CDS can efficiently fine-tune and deploy complex AI models such as LLAMA-3 and DeepSeek.
Fine-Tuning LLAMA-3 and DeepSeek for the Insurance Industry
CDS specializes in data-driven applications for the insurance sector, and with our GPU server, they will be able to fine-tune advanced AI models such as LLAMA-3 and DeepSeek. These models are designed to support a wide range of applications, including:
- Automated Claims Processing: AI-driven automation to assess and process insurance claims efficiently.
- Fraud Detection: Using predictive analytics to identify fraudulent claims in real-time.
- Customer Support Chatbots: Deploying AI-powered virtual assistants to enhance customer experience.
- Risk Assessment: Leveraging AI models to evaluate policyholder risk profiles and optimize underwriting.
Fine-tuning LLMs requires substantial computational power, and with our new GPU server, CDS can process vast amounts of text data while ensuring model accuracy and efficiency.
The Case for On-Premises AI: Security, Cost Efficiency, and Data Control
As companies increasingly rely on AI models for business operations, the question of where to deploy these models has become a crucial consideration. Cloud-based AI solutions, while convenient, pose significant risks in terms of security, cost, and data privacy. At Archsolution, we advocate for on-premises AI infrastructure, offering the following benefits:
1. Enhanced Security and Data Privacy
One of the most significant advantages of running AI models on-premises is data security. When businesses process sensitive data—such as customer records, financial transactions, or proprietary algorithms—sending this information to a cloud provider introduces potential risks, including:
- Data breaches
- Unauthorized access
- Compliance violations
By keeping data on-premises, organizations retain full control over their information, ensuring compliance with industry regulations such as GDPR, HIPAA, and ISO 27001.
2. Avoiding Cloud Vendor Lock-in
Many enterprises find themselves locked into expensive cloud AI services, paying excessive fees for model training, storage, and inference. Cloud providers often charge based on compute hours, API calls, and storage usage, making long-term AI deployment costly. By investing in local AI infrastructure, businesses can:
- Eliminate recurring cloud costs
- Reduce dependence on external vendors
- Gain full ownership of AI models and datasets
3. Optimized Performance with Custom Hardware
Public cloud solutions are often generalized for multiple users, which can lead to performance bottlenecks. With an on-premises setup, businesses can:
- Optimize hardware configurations for specific AI workloads
- Ensure consistent processing speeds without competing for cloud resources
- Customize GPU acceleration based on model complexity
With our AMD EPYC and RTX 3090-powered server, CDS can now run AI workloads without cloud constraints, achieving faster model training times and greater efficiency.
Providing AI Consulting Services with CDS
Beyond infrastructure, we are extending our expertise to help other companies adopt and implement Generative AI (GenAI) solutions. Together with CDS, we provide consulting services tailored to businesses looking to develop their own AI models in-house. Our services include:
- AI Infrastructure Setup: Helping enterprises build and configure GPU-powered AI servers.
- Custom LLM Fine-Tuning: Adapting pre-trained models like LLAMA-3 and DeepSeek to specific business needs.
- On-Premises AI Deployment: Assisting companies in transitioning from cloud AI to local infrastructure.
- AI Security & Compliance: Ensuring AI models comply with industry standards and data protection laws.
Our mission is to democratize AI by enabling businesses to run their own secure, cost-efficient, and high-performance AI systems.
Conclusion: The Future of Enterprise AI is Local
As AI continues to transform industries, businesses must make strategic decisions regarding their AI infrastructure. While cloud-based AI solutions offer accessibility, they come with high costs, security risks, and vendor lock-in. By adopting on-premises AI solutions, organizations can:
- Gain complete control over their AI models and data
- Optimize performance with custom GPU-powered hardware
- Ensure privacy and security by keeping data in-house
- Eliminate expensive cloud fees and long-term dependencies
At Archsolution Limited, we are committed to helping enterprises transition to local AI infrastructure. Whether you need a custom-built GPU server, assistance with fine-tuning LLMs, or consulting for GenAI applications, we are here to support your AI journey.
With our partnership with Clear Data Science Limited, we are ready to help businesses harness the power of AI on their terms—securely, efficiently, and affordably.
If your organization is looking to build AI models on-premises, contact us today and take control of your AI future.