微软 MAI 模型家族：从 1B 到 1T 的全覆盖

# MAI 统一架构 — 缩放感知 Transformer
class MAIArchitecture(nn.Module):
    """微软 MAI 统一架构: 从 1B 到 1T 的缩放感知设计"""
    
    def __init__(self, config: MAIConfig):
        super().__init__()
        # 配置自动适配规模
        self.d_model = config.d_model          # 2048 (1B) ~ 32768 (1T)
        self.n_heads = config.n_heads           # 16 (1B) ~ 256 (1T)
        self.n_layers = config.n_layers         # 24 (1B) ~ 120 (1T)
        self.vocab_size = 128256                # 统一词表
        
        # 核心创新: 缩放感知注意力 (Scale-Aware Attention)
        self.attention = ScaleAwareAttention(
            d_model=self.d_model,
            n_heads=self.n_heads,
            # 小模型: 标准 MHA
            # 中模型: GQA (分组查询)
            # 大模型: MLA (潜在注意力, 类似 DeepSeek)
            mode=config.attention_mode  # 'mha' | 'gqa' | 'mla'
        )
        
        # MoE (仅 XL 和 Ultra)
        if config.use_moe:
            self.moe = MAIMoE(
                n_experts=config.n_experts,     # 64 (XL) ~ 256 (Ultra)
                n_activated=config.n_activated, # 8 (XL) ~ 12 (Ultra)
                d_model=self.d_model
            )
        else:
            self.ffn = SwiGLU(self.d_model, int(self.d_model * 3.5))
        
        # 旋转位置编码 (RoPE) — 全系列统一
        self.rope = RotaryPositionEmbedding(
            dim=self.d_model // self.n_heads,
            max_seq_len=config.max_seq_len,
            scaling=config.rope_scaling  # 'ntk' | 'yarn' | 'dynamic'
        )
        
        # 层归一化: RMSNorm
        self.norm = RMSNorm(self.d_model)

# MAI 训练数据组成
training_data = {
    "web_text": {
        "tokens": "18T (40%)",
        "source": "Common Crawl filtered + MSN 爬取",
        "quality": "经过 3 轮质量过滤"
    },
    "code": {
        "tokens": "8T (18%)",
        "source": "GitHub (top 100万 repo) + 内部代码",
        "languages": "120+ 编程语言"
    },
    "academic": {
        "tokens": "6T (13%)",
        "source": "arXiv + PubMed + 专利库",
        "coverage": "STEM + 社科 + 人文"
    },
    "books": {
        "tokens": "4T (9%)",
        "source": "公有领域 + 授权书籍",
        "quality": "人工筛选"
    },
    "multilingual": {
        "tokens": "5T (11%)",
        "languages": "100+ 语言",
        "note": "中文占 15%"
    },
    "synthetic": {
        "tokens": "4T (9%)",
        "source": "GPT-5 + 内部模型生成",
        "type": "推理数据 + 对话 + 代码"
    },
    "total": "45T tokens"
}

# MAI 模型推理性能基准 (Azure NDv5 实例, 8x H100)
import json

benchmarks = {
    "MAI-1-Small (7B)": {
        "throughput": "320 tokens/s",
        "latency_p50": "45ms",
        "latency_p99": "120ms",
        "concurrent_users": 1000,
        "cost_per_mtok": "$0.08"
    },
    "MAI-1-Medium (32B)": {
        "throughput": "180 tokens/s",
        "latency_p50": "85ms",
        "latency_p99": "210ms",
        "concurrent_users": 500,
        "cost_per_mtok": "$0.25"
    },
    "MAI-1-Large (120B)": {
        "throughput": "95 tokens/s",
        "latency_p50": "180ms",
        "latency_p99": "450ms",
        "concurrent_users": 200,
        "cost_per_mtok": "$0.80"
    },
    "MAI-1-XL (350B MoE)": {
        "throughput": "120 tokens/s",  # MoE 更快
        "latency_p50": "220ms",
        "latency_p99": "580ms",
        "concurrent_users": 150,
        "cost_per_mtok": "$2.50"
    },
    "MAI-1-Ultra (1T MoE)": {
        "throughput": "85 tokens/s",
        "latency_p50": "350ms",
        "latency_p99": "900ms",
        "concurrent_users": 80,
        "cost_per_mtok": "$8.00"
    },
    # 对比: GPT-5 ($10/MTok), Claude Fable 5 ($15/MTok)
    # MAI 在成本上有显著优势
}

# Azure AI — MAI 模型调用示例
from azure.ai.inference import ModelClient
from azure.identity import DefaultAzureCredential

client = ModelClient(
    endpoint="https://ma.azure.com",
    credential=DefaultAzureCredential()
)

# 智能路由: Azure 自动选择最合适的模型
response = client.invoke(
    model="MAI-Auto",  # 智能路由
    messages=[
        {"role": "user", "content": "分析这份财报并给出投资建议"}
    ],
    # Azure Auto Router 根据任务复杂度自动选择:
    # 简单任务 → MAI-Small (低成本)
    # 中等任务 → MAI-Large (平衡)
    # 复杂任务 → MAI-Ultra (最强)
    # 推理任务 → MAI-Reasoner (深度推理)
)

# 也可以手动指定模型
response = client.invoke(
    model="MAI-1-Reasoner",
    messages=[...],
    reasoning={
        "effort": "high",
        "budget_tokens": 8000
    }
)

# 多模型协作
pipeline = client.create_pipeline([
    {"model": "MAI-Vision", "task": "解析图表"},
    {"model": "MAI-Large", "task": "提取关键指标"},
    {"model": "MAI-Reasoner", "task": "综合分析"},
    {"model": "MAI-Code", "task": "生成估值模型代码"}
])

result = pipeline.run(financial_report_image)

2023-2025: 微软 = OpenAI 的渠道商
           ↓
2026:     微软 = AI 全栈厂商 (自研 + OpenAI + 开源)
           ↓
2027(预计): 微软 = 以自研 MAI 为主, OpenAI 为可选

# MAI 对 OpenAI 的威胁评估
threat_analysis = {
    "enterprise_market": {
        "risk_level": "高",
        "reason": "MAI-Large 性能达到 GPT-5 的 92%, 价格仅 1/5",
        "estimated_revenue_impact": "OpenAI 企业营收 -20% (2027)"
    },
    "consumer_market": {
        "risk_level": "低", 
        "reason": "ChatGPT 品牌效应强, 用户粘性高",
        "estimated_revenue_impact": "OpenAI 消费者营收 -5%"
    },
    "developer_market": {
        "risk_level": "中高",
        "reason": "Azure 原生集成 + 低价 + 企业 IT 已锁定 Azure",
        "estimated_revenue_impact": "OpenAI API 营收 -25%"
    },
    "strategic_relationship": {
        "risk_level": "关键",
        "reason": "微软可能减少对 OpenAI 的算力补贴",
        "estimated_cost_impact": "OpenAI 计算成本 +40%"
    }
}

# Microsoft 365 Copilot 2.0 — MAI 驱动
class Copilot20:
    """Copilot 2.0: 智能模型路由 + 本地优先"""
    
    def __init__(self):
        self.router = MAIRouter()
    
    def handle(self, user_request: str, context: dict):
        # 1. 意图分类 (MAI-Nano, 本地)
        intent = self.router.classify(user_request)
        
        # 2. 复杂度评估
        complexity = self.router.estimate_complexity(
            user_request, context
        )
        
        # 3. 模型选择
        if complexity < 0.3:
            model = "MAI-Small"      # 80% 请求
        elif complexity < 0.7:
            model = "MAI-Large"      # 15% 请求  
        else:
            model = "MAI-Reasoner"   # 5% 请求
        
        # 4. 执行
        response = self.execute(model, user_request, context)
        
        # 成本节省: 相比 G-5 驱动的 Copilot 1.0
        # 平均推理成本降低 68%
        # 延迟降低 42%
        # 用户满意度持平