AI 芯片出口管制：中美科技博弈的核心战场

# 2026 年出口管制算力阈值计算示例
def calculate_tpp(chip):
    """
    TPP (Total Processing Performance) 简化计算
    TPP = bit_length × TOPS × 1/1000
    """
    bit_length = chip.get("bits", 16)  # 默认 FP16
    tops = chip.get("tops", 0)
    return bit_length * tops / 1000

def calculate_pd(chip):
    """
    PD (Performance Density) = TPP / die_area (mm²)
    """
    tpp = calculate_tpp(chip)
    die_area = chip.get("die_area", 1)
    return tpp / die_area

# 主流芯片管制状态评估
chips = {
    "NVIDIA H100": {"bits": 16, "tops": 1979, "die_area": 814, "bandwidth_gbs": 900, "memory_tbs": 3.35},
    "NVIDIA H200": {"bits": 16, "tops": 1979, "die_area": 814, "bandwidth_gbs": 900, "memory_tbs": 4.8},
    "NVIDIA B200": {"bits": 16, "tops": 4500, "die_area": 1600, "bandwidth_gbs": 1800, "memory_tbs": 8.0},
    "NVIDIA L20": {"bits": 16, "tops": 1195, "die_area": 814, "bandwidth_gbs": 864, "memory_tbs": 8.0},  # 中国特供
    "AMD MI300X": {"bits": 16, "tops": 1307, "die_area": 1017, "bandwidth_gbs": 896, "memory_tbs": 5.3},
    "华为昇腾910B": {"bits": 16, "tops": 640, "die_area": 600, "bandwidth_gbs": 392, "memory_tbs": 3.2},
}

THRESHOLDS = {"tpp": 4800, "pd": 1.6, "bandwidth_gbs": 600, "memory_tbs": 5.2}

print(f"{'芯片':<18} {'TPP':>8} {'PD':>6} {'带宽GB/s':>10} {'内存TB/s':>10} {'管制状态':>10}")
print("-" * 70)
for name, chip in chips.items():
    tpp = calculate_tpp(chip)
    pd = calculate_pd(chip)
    
    restricted = (tpp >= THRESHOLDS["tpp"] or 
                  pd >= THRESHOLDS["pd"] or 
                  chip["bandwidth_gbs"] >= THRESHOLDS["bandwidth_gbs"])
    
    status = "🔴 管制" if restricted else "🟢 可出口"
    print(f"{name:<18} {tpp:>8.1f} {pd:>6.2f} {chip['bandwidth_gbs']:>10} {chip['memory_tbs']:>10} {status:>10}")

# 特供芯片集群训练效率模拟
import math

def cluster_efficiency(num_gpus, interconnect_bandwidth, compute_tops):
    """
    简化的大规模集群训练效率模型
    效率 = 1 / (1 + overhead_ratio)
    overhead_ratio 与互连带宽成反比，与 GPU 数量成正比
    """
    # 基准：H100 集群的通信开销
    base_bandwidth = 900  # GB/s (NVLink)
    base_overhead = 0.1  # 10% 基准开销（千卡集群）
    
    # 互连带宽影响因子
    bandwidth_ratio = base_bandwidth / max(interconnect_bandwidth, 100)
    
    # GPU 数量影响因子（对数缩放）
    scale_factor = math.log10(max(num_gpus, 10)) / math.log10(1000)
    
    overhead = base_overhead * bandwidth_ratio * scale_factor
    efficiency = 1 / (1 + overhead)
    
    return round(efficiency * 100, 1)

# 8192 卡集群效率对比
cluster_size = 8192
configs = {
    "H100 集群 (全球版)": {"bandwidth": 900, "compute": 1979},
    "H20 集群 (中国特供)": {"bandwidth": 900, "compute": 296},
    "B20 集群 (中国特供)": {"bandwidth": 400, "compute": 2000},
    "L20 集群 (中国特供)": {"bandwidth": 864, "compute": 1195},
    "昇腾910B 集群 (国产)": {"bandwidth": 392, "compute": 640},
}

print(f"8192 卡集群训练效率对比:")
print(f"{'配置':<25} {'单卡算力':>10} {'互连带宽':>10} {'集群效率':>10} {'等效算力':>12}")
print("-" * 72)
for name, cfg in configs.items():
    eff = cluster_efficiency(cluster_size, cfg["bandwidth"], cfg["compute"])
    effective = cfg["compute"] * cluster_size * eff / 100
    print(f"{name:<25} {cfg['compute']:>10} {cfg['bandwidth']:>10} {eff:>9.1f}% {effective:>12,.0f}")

# 云服务管制规则伪代码
def check_cloud_gpu_access(client_info, requested_compute):
    """
    检查客户是否符合高端 GPU 云服务访问条件
    """
    BLOCKED = False
    warnings = []
    
    # 1. 身份审查
    if client_info["country"] in ["China", "Macau", "Hong Kong"]:
        if not client_info.get("end_user_certificate"):
            warnings.append("需要最终用户认证")
        
        # 2. 算力阈值检查
        if requested_compute > 100:  # PFLOPS
            warnings.append("超过 100 PFLOPS 阈值，需商务部审批")
            if not client_info.get("commerce_approval"):
                BLOCKED = True
    
    # 3. IP 地理位置检查
    if client_info.get("ip_country") == "China":
        if requested_compute > 10:  # PFLOPS
            warnings.append("来自中国 IP 的高算力请求需额外审查")
    
    return {"blocked": BLOCKED, "warnings": warnings}

# 示例：中国某公司申请 200 PFLOPS 云算力
result = check_cloud_gpu_access(
    client_info={"country": "China", "ip_country": "China", 
                 "end_user_certificate": True, "commerce_approval": False},
    requested_compute=200
)
print(f"访问结果: {'拒绝' if result['blocked'] else '批准'}")
for w in result["warnings"]:
    print(f"  ⚠️ {w}")

# AI 芯片软件生态对比
ecosystems = {
    "NVIDIA CUDA": {
        "developers": "500万+",
        "libraries": ["cuDNN", "cuBLAS", "NCCL", "TensorRT"],
        "framework_support": "PyTorch/TensorFlow/JAX 原生",
        "maturity": 10,
        "lock_in": "极高"
    },
    "华为 CANN": {
        "developers": "50万+",
        "libraries": ["AscendC", "MindSpore", "MindKits"],
        "framework_support": "PyTorch(适配)/MindSpore",
        "maturity": 6,
        "lock_in": "中"
    },
    "AMD ROCm": {
        "developers": "30万+",
        "libraries": ["MIOpen", "rocBLAS", "RCCL"],
        "framework_support": "PyTorch 原生",
        "maturity": 7,
        "lock_in": "低"
    },
    "Intel oneAPI": {
        "developers": "20万+",
        "libraries": ["oneDNN", "oneCCL"],
        "framework_support": "PyTorch/TensorFlow",
        "maturity": 5,
        "lock_in": "低"
    },
}

print("AI 芯片软件生态成熟度评分:")
for eco, info in ecosystems.items():
    bar = "★" * info["maturity"] + "☆" * (10 - info["maturity"])
    print(f"  {eco:<16} {bar}  开发者: {info['developers']}")

美国 AI 路线: 追求极致规模 → 万亿参数 → 百万卡集群
                    ↓
中国 AI 路线: 追求极致效率 → 模型蒸馏 → MoE 优化 → 算法创新