**Role:** You are an expert in local LLM deployment, privacy...
Prompt
**Role:** You are an expert in local LLM deployment, privacy engineering, and hybrid AI architectures. Give me a practical, step-by-step plan tailored to my exact hardware. ### My Hardware - **CPU:** AMD Ryzen 3 3200G (4 cores / 4 threads, Zen+, integrated Vega 8) - **RAM:** 32 GB DDR4 (2×16 GB, 3200 MHz, dual-channel) - **GPU:** AMD Radeon RX 570, 4 GB VRAM (Polaris / GCN 4, not officially supported by ROCm) - **OS:** [fill in: Windows 10/11 or Linux distro] ### My Priorities 1. **Intelligence over speed.** Slow generation (even 1–3 tokens/sec) is acceptable. I want the smartest model my system can realistically run. 2. **Maximum privacy.** I have a serious medical condition, and my data (MRI results, diagnoses, lab reports, medications, doctor's notes) is extremely sensitive. No identifiable information should ever leave my machine. ### What I Want to Build: A Hybrid Privacy-First AI System - **Local AI (default):** Handles every task it can manage on its own. - **Anonymization layer:** When a task is too complex, the local AI removes or pseudonymizes all personal and identifying data (names, dates, locations, ID numbers, facility names, rare details that could re-identify me) before anything is sent out. - **Frontier API (fallback):** The anonymized request goes to a large cloud model (e.g., Claude, GPT, Gemini) for the complex reasoning. - **Re-identification (local):** The response comes back, and the local system maps the placeholders back to my real data so the final answer is personalized, all offline. - **Routing:** The system (or I, via a simple choice) decides when a task is "local-capable" vs. "needs frontier model." ### Please Answer 1. **Model selection:** Which local models and sizes (e.g., 7B, 14B, 32B) and quantizations (Q4_K_M, Q5, Q8, etc.) give the best intelligence within 32 GB RAM + 4 GB VRAM? Are medical-tuned models worth it? List the top options with expected speed. 2. **Inference software:** What's the best backend for my AMD RX 570 (llama.cpp with Vulkan, KoboldCpp, LM Studio, Ollama)? How do I split layers between GPU and CPU? 3. **Anonymization:** How reliable is LLM-based anonymization? Should I combine it with rule-based tools (e.g., Microsoft Presidio, regex, spaCy NER)? How do I handle medical documents specifically, including PDFs and scanned images (OCR)? 4. **Architecture:** Propose a concrete setup (tools, scripts, or frameworks like Open WebUI, LiteLLM, or a custom Python pipeline) that implements the local, anonymize, API, and re-identify flow. 5. **Routing logic:** How should the system decide between local and cloud? Should it be automatic, manual, or local-first with a confidence check? 6. **Risks:** What privacy risks remain even after anonymization (re-identification through rare conditions, metadata, API provider data retention)? How can I minimize them (e.g., providers with zero-retention policies, a manual review step before sending)? 7. **Upgrade path:** If I later upgrade one component, what gives the biggest intelligence gain per dollar (more RAM, a used GPU with more VRAM, etc.)? ### Output Format - Start with a short recommended setup summary. - Then give step-by-step installation instructions. - Include example code or config where useful (e.g., a Python anonymization + API routing script). - Be honest about limitations of my hardware. Don't oversell. --- ### Tips before sending - Fill in your **operating system**, because it changes the instructions significantly. - Mention your **technical skill level** (beginner / can run scripts / programmer). - If you have a preferred **frontier API provider** or a budget for API costs, add it.
