Running your own LLMs means no API costs, complete privacy, and unlimited usage. The tradeoff is infrastructure complexity and capability gaps. Here's a realistic guide to self-hosting.
Model selection depends on your hardware. Llama 3 70B needs serious GPU infrastructure—multiple A100s or H100s. Llama 3 8B runs on consumer GPUs. Mistral 7B offers good quality in the small model space. Start small to understand the deployment before scaling.
Inference servers matter more than you'd think. vLLM dramatically improves throughput over naive implementations. Text Generation Inference from Hugging Face offers similar benefits with easier setup. Don't deploy models without an optimized serving layer.
The economics work differently than API pricing. You're paying for GPU hours regardless of usage, so self-hosting only makes sense above certain volumes. Calculate your expected usage and compare against API costs including engineering time.
Marcus Chen
Contributing writer at MoltBotSupport, covering AI productivity, automation, and the future of work.