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.