Your Frontier Prompt Breaks on a Local Model. Take It Apart

You wrote a long system prompt for a frontier model and it works. Paste it into a local model and it forgets rules, ignores the output format, repeats tool calls and sometimes invents things. A September 2026 write-up on the Hacker News front page described 35 KB preprompts that used 14 percent of a 65k-token Ollama window before any work began. The bigiron.cc guide's answer: don't translate the prompt, take it apart and move most of it out of the prompt. It also tests the common claim that local models just follow fewer rules: on a Qwen-family 27B at Q4_K_M, five simple rules were met 10 of 10 times even when buried among 155 rules. The sample is small and the rules were easy, but counting rules turns out to be the wrong diagnosis. What's covered: • When a port is worth it, and when to keep the job on the API • A smaller, shared context window, and Ollama's 4,096-token default under 24 GiB of VRAM • Why every turn can pay for the whole prompt again • The rule-dilution test: 5, 25, 60 and 155 rules, ten runs each • What actually breaks ports: fit, judgment rules, conflicts, weak spots • Counting, declining and self-reported success as known weak spots • Step 1: a regression set checked by code, and a frontier baseline • Step 2: inventory the prompt and decide where each kind goes • Step 3: move formats, limits and arithmetic into code and schemas • Step 4: a router plus single-objective specialist prompts • Step 5: short positive contracts with escape hatches • Step 6: thinking, samplers, context and a stable prefix, set explicitly • Step 7: iterate to a pass rate chosen in advance • An illustrative worked example: a household paperwork agent • When to stop porting, and the local-plus-fallback end state ⏱️ Chapters 0:00 Port a Prompt Locally 1:53 Why Prompts Break 4:49 Measure First 6:36 Take It Apart 8:38 Settings and Done 9:53 A Worked Example 📖 The full guide (the promptcheck.py regression runner, the prompt inventory script, the rule-dilution test script and a troubleshooting table): → Porting a Long System Prompt From a Frontier Model to a Local Model: https://www.bigiron.cc/guides/porting... 🛠️ Gear (affiliate — same price to you, supports the channel): • 24 GB-class GPU for a 27B model → https://www.amazon.com/s?k=rtx+3090+2... • 12 GB-class GPU for smaller router models → https://www.amazon.com/s?k=rtx+3060+1... • 64 GB RAM kit for a local model box → https://www.amazon.com/s?k=64gb+ddr5+... As an Amazon Associate we earn from qualifying purchases. Affiliate links — same price to you, supports the channel. 👉 Subscribe for self-hosted AI + homelab, with new videos every day. #LocalAI #Ollama #LLM #PromptEngineering #SystemPrompt #llamacpp #SelfHosted #Homelab #Agents #AI