当AI推理成为商品：速度竞赛背后的产业变革与开发者新选择

# 推理优化技术栈示意
optimization_stack = {
    "hardware": {
        "cerebras": "晶圆级引擎，750 tokens/s",
        "nvidia_blackwell": "NVFP4内核，专用推理加速",
        "groq": "LPU架构，超低延迟"
    },
    "model_architecture": {
        "moe": "混合专家，容量与计算解耦",
        "speculative_decoding": "多token预测，吞吐量倍增",
        "mtp": "训练阶段内置多token预测能力"
    },
    "quantization": {
        "nvfp4": "4位浮点，内存减半，精度损失<1%",
        "bf16": "16位浮点，最高精度基准",
        "dynamic": "动态量化，按层自适应精度"
    },
    "routing": {
        "nemo_switchyard": "智能任务路由",
        "cost_aware": "成本感知路由",
        "latency_aware": "延迟感知路由"
    }
}

# 每一层优化都可以独立或组合使用
# 实际效果是乘法关系：4x(硬件) * 2x(架构) * 1.5x(量化) = 12x总体提升

# 速度层级选择决策树
def choose_inference_tier(task_type, latency_requirement, budget):
    """
    根据任务类型和延迟要求选择推理层级
    """
    if task_type == "real_time_interaction":
        # 实时交互：语音、客服、直播推荐
        if budget == "high":
            return "ultrafast"  # 750 tokens/s
        else:
            return "standard"   # 基准速度，成本优先
    
    elif task_type == "near_real_time":
        # 准实时：代码补全、搜索增强
        return "standard"
    
    elif task_type == "batch_processing":
        # 批量处理：文档分析、数据清洗
        return "batch"  # 最低成本层级
    
    elif task_type == "agent_execution":
        # Agent执行层：工具调用、结果验证
        return "lightning_model"  # 高效小模型
    
    elif task_type == "agent_planning":
        # Agent规划层：复杂推理
        return "frontier_model"  # 前沿推理模型

# 生产级模型路由配置示例
ROUTING_POLICY = {
    "rules": [
        {
            "condition": {"task": "code_generation", "complexity": "high"},
            "route_to": "claude-sonnet",
            "reason": "高质量代码生成"
        },
        {
            "condition": {"task": "tool_call", "frequency": "high"},
            "route_to": "nemotron-3.5-lightning",
            "reason": "高频执行层，成本优先"
        },
        {
            "condition": {"task": "summarization", "context_length": ">100k"},
            "route_to": "gpt-5.6-sol",
            "reason": "长上下文处理能力强"
        },
        {
            "condition": {"task": "any", "budget_remaining": "<10%"},
            "route_to": "deepseek-v4-flash",
            "reason": "预算紧张时切换到最低成本选项"
        }
    ],
    "fallback": "nemotron-3.5-lightning",
    "monitoring": {
        "track_cost_per_task": True,
        "track_latency_p99": True,
        "auto_switch_threshold": {
            "cost_per_day": 100,  # 美元
            "latency_p99": 5.0    # 秒
        }
    }
}