Ollama settings boost local LLM performance by preventing CPU fallback

This article provides a guide to optimizing Ollama for local LLM execution, specifically addressing issues where models silently fall back to CPU usage despite having a capable GPU. It explains that the problem often stems from the KV cache exceeding available VRAM, not the model weights themselves. The guide details four key settings: enabling `OLLAMA_FLASH_ATTENTION`, quantizing the KV cache with `OLLAMA_KV_CACHE_TYPE=q8_0`, limiting parallel requests with `OLLAMA_NUM_PARALLEL=1`, and explicitly setting `num_ctx` to a manageable value. By implementing these adjustments, users can achieve full GPU offloading and significantly improve inference speeds, transforming models from unusable to genuinely useful for tasks like coding. AI

IMPACT Enables users to run larger LLMs locally on consumer hardware, reducing reliance on cloud services.

RANK_REASON The article provides practical tips and configuration settings for optimizing existing software (Ollama) for better performance.

Read on dev.to — LLM tag →

• 7B Model
• central processing unit
• graphics processing unit
• Llama-3.1:8b
• Mac
• Ollama
• Qwen2.5-Coder 7B

• infra
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →