Ollama beginner asks for faster local setup

The main bottleneck here is orchestration, not just model quality: a 16K context on 8GB VRAM is likely eating the headroom that would otherwise keep the model responsive. If you want cloud-like behavior, you need an agent loop plus tool calling, not just a prompt wrapper around search.

• –Ollama’s docs recommend much smaller context on low-VRAM systems by default, and only push large contexts when the hardware can actually hold them comfortably.
• –ChatGPT-style “decide, search, answer” behavior comes from a multi-turn tool-calling loop; the model itself will not magically browse unless the app keeps handing it tools and results.
• –Better formatting usually comes from short, explicit style rules and examples, not from pasting a huge system prompt wholesale.
• –For an 8GB card, a smaller or more aggressively quantized model will often feel faster than forcing a bigger context window first.
• –“Local search” only helps if you are searching your own corpus; for the public web, local retrieval can organize results, but it cannot remove network latency.