Running Japanese LLMs on a Shoestring: Practical Guide for 2GB VPS

This article provides a pragmatic, hands-on approach to deploying Japanese LLMs on resource-constrained VPS environments. The emphasis on model selection (1B parameter models), quantization (Q4), and careful configuration of llama.cpp offers a valuable starting point for developers looking to experiment with LLMs on limited hardware and cloud resources. Further analysis on latency and inference speed benchmarks would strengthen the practical value.

• •Demonstrates the possibility of running Japanese LLMs on 2GB RAM VPS.
• •Highlights the importance of GGUF quantization (specifically Q4) for resource optimization.
• •Emphasizes the need for careful configuration of llama.cpp and KV cache.