RTX 5060 Ti 16GB tests context limits

The real bottleneck here is KV-cache budget, not just raw VRAM. On 16GB, brute-forcing bigger context usually hurts more than it helps unless you also manage conversation history aggressively.

• –Summarize or trim older turns; keep only the active task state in the prompt.
• –Use retrieval or external memory for long-lived facts instead of replaying the entire conversation every turn.
• –Bigger context windows are useful, but they consume VRAM linearly and can push you into slower inference or smaller quants.
• –For llama.cpp setups, tune context size, cache behavior, and prompt reuse before assuming you need more hardware.
• –Workflows like n8n should separate short-term chat from long-term memory or they will balloon quickly.