As open-source LLMs become more capable, many teams are exploring local deployment for privacy, latency, and cost reasons. This guide offers a systematic approach to hardware selection, covering GPU, RAM, and storage requirements for models from 7B to 70B parameters. It includes a four-coordinate framework (model size, inference speed, batch size, budget) and a capacity formula to estimate memory needs. Verified benchmarks from real-world deployments help developers make informed trade-offs between performance and cost. The guide also addresses emerging trends like multi-GPU setups and quantization. For engineering leaders, this is a valuable reference for planning on-premise AI infrastructure.
A comprehensive guide on selecting hardware for local LLM deployment, including capacity formulas and verified benchmarks.