Keeping Self-Hosted LLM Costs Down: Best Practices and Tips

Large Language Models (LLMs) are increasingly utilized across various applications, but the costs of self-hosting these models can be prohibitive, especially for mid-sized companies. To manage these expenses, it is essential to choose an appropriately sized model for specific tasks, as smaller models like DistilBERT can suffice for simpler applications, whereas more complex tasks may require larger models like GPT-3. Efficient resource allocation is crucial, with strategies such as leveraging GPUs effectively, using virtualization and containerization for dynamic resource management, and implementing data parallelism to optimize hardware usage. Model compression techniques like quantization, pruning, knowledge distillation, and low-rank factorization can also reduce computational demands and costs. Effective data management practices, including preprocessing, caching, and utilizing data lakes or warehouses, further minimize storage and processing costs. These strategies can be combined to achieve significant cost savings in self-hosting LLMs.