A User's Personal Journey and Evolution with Local LLMs and llama.cpp
A community member shared a retrospective detailing their personal transition from a cloud LLM skeptic in early 2025 to running fully local workflows using tools like llama.cpp and SillyTavern by 2026. Over this period, they upgraded their hardware to a dual RTX 5060 Ti setup and witnessed massive performance gains in text generation and processing speed. This testimonial highlights the practical viability, hardware evolution, and rapid performance advancements of local AI tools over a short 1.5-year span. It demonstrates how everyday users can now completely replace cloud-dependent services for complex tasks like coding and roleplay. The user's setup transitioned through various hardware configurations, ultimately utilizing a dual ASUS PRIME RTX 5060 Ti 16GB setup taking advantage of tensor split and Multi-Token Prediction (MTP) in llama.cpp. By early 2026, generation speeds for models like Gemma 4 31B and Qwen3.8 27B exceeded 60 to 70 tokens per second.
## BACKGROUND
llama.cpp is a widely used open-source software library that performs efficient inference on large language models locally, often serving as the backbone for community frontends and tools like Ollama, LM Studio, and SillyTavern. SillyTavern is a popular open-source LLM frontend tailored for interactive roleplay and creative storytelling.