Setting Up Your On-Premise Environment for LLMs

Large Language Models (LLMs) like GPT, BERT, Deepseek, Gemini and others have revolutionized how we interact with AI. Whether you're a researcher, developer, or business aiming to harness these models, setting up an on-premise environment can be a game-changer.

This article will guide you through the essential steps to get your on-premise setup running smoothly. We’ll cover:

• Hardware requirements – Understanding the computing power needed.
• GPU selection – Choosing the right GPU for training and inference.
• Software setup – Installing frameworks, drivers, and libraries.
• Performance tuning – Preparing your system for high-speed inference.

With the right setup, you can unlock the full potential of LLMs while maintaining control over your infrastructure.

Hardware Requirements for Self-Hosting: GPUs, Storage, Networking

GPUs: The Heart of LLM Inference

When it comes to running LLMs, Graphics Processing Units (GPUs) are the backbone of your setup. Unlike CPUs, GPUs are designed to handle parallel processing tasks, making them ideal for the heavy computational load required by LLMs.

Storage: Where Your Data Lives

LLMs are data-hungry, and you'll need ample storage to house your models, datasets, and logs.

• SSD vs. HDD: Solid State Drives (SSDs) are preferred over Hard Disk Drives (HDDs) due to their faster read/write speeds, which are crucial for loading large models and datasets quickly.
• Capacity: Depending on the size of your models and datasets, you may need several terabytes of storage. For example, GPT-3's model weights alone can take up hundreds of gigabytes.

Networking: The Backbone of Distributed Systems

If you're planning to run a distributed setup (e.g., multi-node training or inference), networking becomes critical.

• High-Speed Interconnects: Consider using high-speed networking hardware like InfiniBand or 10/25/100 Gigabit Ethernet to ensure low-latency communication between nodes.
• Bandwidth: Ensure your network has sufficient bandwidth to handle the data transfer requirements, especially if you're working with large datasets or models.

Choosing the Right GPU (e.g., NVIDIA Tesla T4, A100, etc.)

Selecting the right GPU is crucial for optimizing performance and cost. Here are some popular options:

NVIDIA H100 (SXM and PCIe variants)

• VRAM: 80 GB HBM3
• Performance: State-of-the-art training and inference speeds powered by an advanced Transformer Engine and massive tensor core acceleration.
• Form Factors: Available in both SXM (for maximum throughput in high-density data centers) and PCIe (offering greater flexibility and power efficiency for development and smaller-scale deployments).
• Use Case: Ideal for enterprise-level applications and large-scale model training as well as robust inference in both massive and compact environments.

AMD Instinct MI300X

• VRAM: 192 GB HBM3
• Performance: Exceptional throughput with up to 653.7 TFLOPS (FP16), engineered to accelerate next-generation AI workloads.
• Use Case: Perfect for training and deploying large-scale language models, high-performance computing tasks, and inference-intensive applications across enterprise data centers.

Initial Software Setup: Docker, Kubernetes, and MinIO for Model Storage

Preparing Your System for High-Performance Inference

Optimizing GPU Utilization

To get the most out of your GPUs, you'll need to optimize their utilization.

• Mixed Precision Training: Use mixed precision (FP16) to reduce memory usage and increase computational speed.
• CUDA and cuDNN: Ensure you have installed the latest versions of CUDA and cuDNN to take full advantage of your GPU's capabilities.

Load Balancing and Scaling

For high-performance inference, load balancing and scaling are essential.

• Horizontal Scaling: Distribute the load across multiple GPUs or nodes to handle more requests simultaneously.
• Vertical Scaling: Increase the resources (e.g., GPU memory, CPU cores) on a single node to handle larger models or more complex computations.

Monitoring and Logging

Keeping an eye on your system's performance is crucial for maintaining high availability and performance.

• Monitoring Tools: Use tools like Prometheus and Grafana to monitor GPU utilization, memory usage, and network performance.
• Logging: Implement centralized logging using tools like ELK Stack (Elasticsearch, Logstash, Kibana) to track errors and performance metrics.

Grafana dashboard monitoring GPU utilization and memory usage.

Conclusion

Setting up an on-premise environment for LLMs can be complex but highly rewarding. With the right hardware, optimized software, and performance-focused setup, you can build a robust system to handle demanding AI workloads.

Previous: If you haven't read our previous article then Click here

Next Up: “Hosting LLMs with Ollama, Mistral, and VLM: Practical Tools for Deployment” Stay tuned!