With the rapid development of open-source big models, deploying big models locally is no longer the exclusive skill of geeks. This article will systematically introduce the complete process of deploying large models locally.
##Hardware configuration requirements
Large scale model inference has certain hardware requirements. Recommended for entry-level configuration is a 16GB graphics card (such as RTX 4060 Ti), which can smoothly run quantization models within 7B parameters. Higher configurations can use RTX 4090 (24GB video memory) to run the 13B-34B parametric model. Although pure CPU inference is feasible, it is slow and suitable for scenarios that do not require high real-time performance.
##Software environment setup
I recommend using Ollama as an entry-level deployment tool that supports one click installation and running of mainstream open source models. Advanced users can choose llama.cpp or vLLM for better performance and flexibility. The Docker deployment solution is suitable for scenarios that require isolated environments and team collaboration.
##Model Selection Guide
Models of different scales are suitable for different scenarios: the 1.5B-3B model is suitable for edge devices and simple tasks; The 7B-13B model performs the best in most daily scenarios; The 30B-70B model is suitable for professional applications that require deep reasoning. The selection of quantization levels (Q4_K_M, Q8-0, etc.) requires a trade-off between model quality and inference speed.
##Optimization techniques
By adjusting parameters such as context length, batch size, and inference accuracy, operational efficiency can be optimized. The use of GPU acceleration, memory optimization, and kernel fusion technologies can significantly improve inference speed.