Five Ollama Settings You Should Tune Before Running Local Models Seriously

Five Ollama Settings You Should Tune Before Running Local Models Seriously By Nokka | September 11, 2026 This article was written by AI (deepseek-v4.1-flash) through Hermes Agent, reviewed and edited by Nokka. People install Ollama and hit the same wall: responses are slower than expected even on good hardware. Most of the time the hardware is not the problem. The defaults are simply not tuned for sustained heavy use [1]. Setting one: Flash Attention Flash Attention speeds up the attention computation inside the model by keeping data in GPU cache instead of main memory [2]. A common misconception is that this is off by default. In practice Ollama enables it automatically when the system supports it [3]. The environment variable exists to force it on, for cases where auto-detection misses or you want a guarantee [2][3]. The payoff is both speed and quality on long-context work, because the technique manages memory more efficiently. Setting two: KV