本地多模型协同推理Pipeline设计模板

You are an expert in designing local multi-model collaborative inference pipelines. I need you to design a complete pipeline architecture for my use case. ## My Requirements - **Task type**: [e.g., code generation, document analysis, multi-turn conversation] - **Available models**: [e.g., Qwen3-72B, Llama-3.1-8B, Phi-4-mini] - **Hardware**: [e.g., 2x RTX 4090, M4 Max 128GB, 8x H100] - **Latency target**: [e.g., <2s first token, <10s full response] - **Quality threshold**: [e.g., must match GPT-4o on coding benchmarks] ## Design the Pipeline ### 1. Model Role Assignment For each model, define its role: - **Router model**: Which model classifies/routes incoming requests? - **Draft model**: Which generates initial fast responses? - **Verifier model**: Which validates and refines outputs? - **Specialist models**: Any domain-specific models? ### 2. Orchestration Strategy Choose and detail the pattern: - **Cascade**: Small model first, escalate to larger if confidence < threshold - **Speculative decoding**: Draft model proposes, verifier accepts/rejects tokens - **Mixture of Agents**: Multiple models generate, aggregator synthesizes - **Router-based**: Classify request complexity, route to appropriate model - **Ensemble**: Run multiple models in parallel, vote/merge results ### 3. Implementation Spec Provide concrete YAML configuration with model names, roles, GPU allocation, routing rules, fallback strategies, and monitoring metrics. ### 4. Quality-Cost Tradeoff Analysis Comparison table: Strategy vs Latency vs Quality vs GPU Util vs Cost/1K queries ### 5. Failure Handling - Model OOM recovery - Server crash mid-inference handling - Graceful degradation under overload Be specific with actual model names, quantization levels (Q4_K_M, AWQ, GPTQ, FP16), and serving framework configurations.