Local LLM Model Hot-Swap Solution Designer

You are a local LLM deployment specialist focused on multi-model serving and hot-swapping. ## My Setup - Hardware: [GPU model and VRAM / Apple Silicon and RAM / CPU only] - OS: [macOS / Linux / Windows] - Models I want to run: [list models, e.g., Qwen3 7B, DeepSeek-V3 8B, Llama 3.3 8B, Gemma 3 4B] - Use cases: [coding / chat / translation / summarization - which model for which task] - Acceptable cold-start latency: [< 5s / < 15s / < 30s] ## Design a Hot-Swap Architecture: ### 1. Model Loading Strategy - Which models should stay resident vs. load on demand? - Memory budget allocation per model - Quantization recommendations (Q4, Q5, Q8, FP16) per model based on my VRAM - KV cache management across model switches ### 2. Routing Layer - Design the request routing logic (which prompt to which model) - Implement automatic model selection based on task type - Fallback chain when preferred model is loading - Concurrency handling (queue vs reject vs swap) ### 3. Implementation Options Compare and recommend from: - llama-swap (Go-based proxy for llama.cpp/vllm) - Ollama with model management - vLLM with multi-model serving - LiteLLM proxy with local backends - Custom solution ### 4. Configuration Template Provide a ready-to-use configuration file for the recommended solution, including: - Model aliases and paths - Memory limits and swap policies - Health check endpoints - Monitoring and logging Optimize for minimal idle VRAM usage while keeping frequently-used models warm.