Hardware Requirements for Large Language Models

Hardware Requirements for Large Language Models

What hardware is needed for large language models? The optimal hardware configuration depends heavily on the size of the LLM and its intended use (inference, training, fine-tuning). However, a strong foundation relies on a server-grade platform. We strongly recommend using either Intel Xeon or AMD EPYC processors. These platforms offer crucial features for optimal LLM performance including numerous PCI-Express lanes for connecting GPUs and storage devices, high memory bandwidth and capacity, and ECC memory support, which is vital for data integrity.

GPU Recommendations

For large language models, the GPU is the most critical component. Professional or compute-level GPUs are necessary due to their higher VRAM capacity and suitability for server environments. NVIDIA's RTX 6000 Ada, L40S, and H100, along with AMD's MI Instinct GPUs, are excellent examples. The total VRAM required is directly proportional to the LLM's size; a model like Llama3-70b might require approximately 200GB of VRAM for efficient multi-user serving. Using multiple GPUs significantly improves performance; a typical system might include 4-8 GPUs. While NVIDIA maintains a historical lead in GPU computing for AI, AMD’s ROCm is gaining traction, supported by platforms like Hugging Face and PyTorch. Further details on GPU selection can be found here.

Memory (RAM)Requirements

System RAM is crucial for efficient data buffering. A good rule of thumb, as recommended by NVIDIA and Puget Systems, is to have at least twice the amount of system RAM as total GPU VRAM. This ensures efficient "memory pinning" to CPU space, optimizing performance. Insufficient RAM can lead to bottlenecks and reduced performance.

Storage Considerations

High-capacity NVMe solid-state drives (SSDs)are recommended for storing LLM parameters and datasets. Models and associated data can consume significant storage space; 2-8TB NVMe SSDs per server are often needed. Additional NVMe SSDs, potentially configured in software-controlled arrays for data redundancy, may be required. While network-attached storage (NAS)can be used for backup or data sharing across multiple systems, keeping LLM parameters locally on the server is essential for optimal performance. Learn more about storage solutions for high-performance computing here.