Conway Improves Local LLM Performance

What happened

XDA’s Lead Technical Editor, Adam Conway, detailed eight underutilised settings for local large language models (LLMs) that improve performance and output quality. These parameters, including temperature, min-p, context length (num_ctx), KV cache quantisation, repetition/presence penalties, chat templates, and Flash Attention, address issues like repetitive responses and context loss. Adjusting these in tools such as Ollama and llama.cpp can halve VRAM usage for context with Q8_0 KV cache quantisation or accelerate inference for longer sequences via Flash Attention. Incorrect chat templates cause models to ignore system prompts; disabling "thinking mode" reduces inference time.

Why it matters

Improving local LLM deployments requires moving beyond default settings, impacting operational efficiency and resource allocation. Platform engineers and architects reduce VRAM consumption through KV cache quantisation, halving memory use at Q8_0 with negligible quality impact. Faster inference, particularly with Flash Attention for longer contexts, cuts processing times, improving developer productivity. This follows Hugging Face securing the llama.cpp team, underscoring efficient local model execution. Ignoring these parameters leads to suboptimal model performance, increased hardware demands, and wasted compute cycles, affecting project timelines and infrastructure costs. Teams should audit current local LLM configurations.