Meta Compute：AI算力军备竞赛从"囤卡"到"卖卡"的范式转折

package main

import (
	"fmt"
	"math"
	"time"
)

// GPUAsset 算力资产模型
type GPUAsset struct {
	Name         string
	UnitCost     float64 // 单卡采购成本（美元）
	TotalUnits   int     // 总卡数
	PowerKW      float64 // 单卡功耗（kW）
	Electricity  float64 // 电价（美元/kWh）
	LifecycleMonths int  // 生命周期（月）
	DepreciationRate float64 // 月折旧率
}

// ComputeCluster 算力集群
type ComputeCluster struct {
	Assets      []GPUAsset
	UtilRate    float64 // 内部利用率
	RentRate    float64 // 对外出租比例
	RentPrice   float64 // 出租价格（美元/卡/小时）
}

// AnnualCostAnalysis 年度成本收益分析
func (c *ComputeCluster) AnnualCostAnalysis() map[string]float64 {
	result := make(map[string]float64)
	var totalProcurement, totalPower, totalDepreciation float64

	for _, asset := range c.Assets {
		// 采购成本
		procurement := asset.UnitCost * float64(asset.TotalUnits)
		totalProcurement += procurement

		// 年电费（按24小时运行，365天）
		annualPower := asset.PowerKW * 24 * 365 * asset.Electricity * float64(asset.TotalUnits)
		totalPower += annualPower

		// 年折旧（直线折旧）
		monthlyDep := procurement / float64(asset.LifecycleMonths)
		totalDepreciation += monthlyDep * 12
	}

	// 内部使用价值（按利用率折算）
	internalValue := totalDepreciation * c.UtilRate

	// 对外出租收入（假设出租部分满租）
	rentableUnits := 0
	for _, asset := range c.Assets {
		rentableUnits += int(float64(asset.TotalUnits) * c.RentRate)
	}
	rentalIncome := float64(rentableUnits) * c.RentPrice * 24 * 365

	result["totalProcurement"] = totalProcurement
	result["totalPower"] = totalPower
	result["totalDepreciation"] = totalDepreciation
	result["internalValue"] = internalValue
	result["rentalIncome"] = rentalIncome
	result["netCost"] = totalDepreciation + totalPower - rentalIncome
	result["rentalCoverage"] = rentalIncome / totalDepreciation * 100

	return result
}

func main() {
	// Meta 2026年算力资产模拟
	cluster := ComputeCluster{
		Assets: []GPUAsset{
			{
				Name:         "H100 GPU Cluster",
				UnitCost:     30000,
				TotalUnits:   500000,
				PowerKW:      0.7,
				Electricity:  0.08,
				LifecycleMonths: 48,
			},
			{
				Name:         "GB200 GPU Cluster",
				UnitCost:     50000,
				TotalUnits:   300000,
				PowerKW:      1.2,
				Electricity:  0.08,
				LifecycleMonths: 60,
			},
		},
		UtilRate:  0.65,  // 内部利用率65%
		RentRate:  0.25,  // 25%闲置算力出租
		RentPrice: 2.0,   // $2/卡/小时
	}

	analysis := cluster.AnnualCostAnalysis()

	fmt.Println("========== Meta Compute 年度成本收益分析 ==========")
	fmt.Printf("总采购成本: $%.2f亿\n", analysis["totalProcurement"]/1e8)
	fmt.Printf("年电费支出: $%.2f亿\n", analysis["totalPower"]/1e8)
	fmt.Printf("年折旧成本: $%.2f亿\n", analysis["totalDepreciation"]/1e8)
	fmt.Printf("内部使用价值: $%.2f亿\n", analysis["internalValue"]/1e8)
	fmt.Printf("对外出租收入: $%.2f亿\n", analysis["rentalIncome"]/1e8)
	fmt.Printf("净成本（折旧+电费-租金）: $%.2f亿\n", analysis["netCost"]/1e8)
	fmt.Printf("租金对折旧的覆盖率: %.2f%%\n", analysis["rentalCoverage"])

	// 敏感性分析
	fmt.Println("\n--- 租金价格敏感性分析 ---")
	for _, price := range []float64{1.5, 2.0, 2.5, 3.0} {
		cluster.RentPrice = price
		rev := cluster.AnnualCostAnalysis()["rentalIncome"]
		fmt.Printf("租金 $%.2f/卡/小时 → 年收入 $%.2f亿\n", price, rev/1e8)
	}
}

"""
Llama生态变现模型 - MaaS定价与成本分析
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List

@dataclass
class ModelSpec:
    name: str
    params_b: float  # 参数规模（十亿）
    context_len: int  # 上下文长度
    flops_per_token: float  # 每Token计算量（TFLOPs）
    
class MetaComputePricing:
    """Meta Compute MaaS定价引擎"""
    
    def __init__(self, gpu_cost_per_hour: float = 3.5):
        self.gpu_cost = gpu_cost_per_hour
        self.models = {
            "llama-4-8b": ModelSpec("Llama 4 8B", 8, 131072, 0.8),
            "llama-4-70b": ModelSpec("Llama 4 70B", 70, 131072, 7.0),
            "llama-4-405b": ModelSpec("Llama 4 405B", 405, 131072, 40.5),
            "muse-spark-v1": ModelSpec("Muse Spark V1", 180, 65536, 18.0),
        }
        
    def calculate_inference_cost(self, 
                                 model_name: str,
                                 input_tokens: int,
                                 output_tokens: int,
                                 batch_size: int = 1,
                                 gpu_count: int = 1) -> Dict:
        """计算推理成本"""
        model = self.models[model_name]
        
        # 推理计算量 = 前向传播FLOPs
        # 简化模型：每Token约 2*参数量 FLOPs
        prefill_flops = input_tokens * model.params_b * 1e9 * 2
        decode_flops = output_tokens * model.params_b * 1e9 * 2
        total_flops = prefill_flops + decode_flops
        
        # GPU理论算力（H100 FP8: 1979 TFLOPS）
        gpu_tflops = 1979.0
        gpu_utilization = 0.45  # 实际利用率
        
        # 计算时间（秒）
        compute_time = total_flops / (gpu_tflops * 1e12 * gpu_utilization * gpu_count)
        
        # 成本核算
        cost = compute_time / 3600 * self.gpu_cost * gpu_count
        cost_per_million_input = (cost / input_tokens) * 1_000_000 if input_tokens > 0 else 0
        cost_per_million_output = (cost / output_tokens) * 1_000_000 if output_tokens > 0 else 0
        
        # 对外定价（成本 + 40%毛利率）
        markup = 1.4
        price_input = cost_per_million_input * markup
        price_output = cost_per_million_output * markup
        
        return {
            "model": model_name,
            "total_flops": total_flops,
            "compute_time_s": compute_time,
            "cost_usd": cost,
            "price_per_million_input_tokens": price_input,
            "price_per_million_output_tokens": price_output,
            "recommended_price_input": f"${price_input:.4f}/M tokens",
            "recommended_price_output": f"${price_output:.4f}/M tokens",
        }
    
    def batch_pricing_table(self) -> List[Dict]:
        """生成完整定价表"""
        results = []
        scenarios = [
            ("llama-4-8b", 1000, 500, 8),
            ("llama-4-70b", 1000, 500, 4),
            ("llama-4-405b", 1000, 500, 1),
            ("muse-spark-v1", 2000, 1000, 2),
        ]
        for model, inp, out, gpus in scenarios:
            r = self.calculate_inference_cost(model, inp, out, gpu_count=gpus)
            results.append(r)
        return results

# 生成定价分析
pricing = MetaComputePricing()
table = pricing.batch_pricing_table()

print("=" * 90)
print(f"{'模型':<20} {'输入成本':<20} {'输出成本':<20} {'单次成本':<15} {'计算时间':<15}")
print("=" * 90)
for row in table:
    print(f"{row['model']:<20} {row['price_per_million_input_tokens']:<20.4f} "
          f"{row['price_per_million_output_tokens']:<20.4f} "
          f"{row['cost_usd']:<15.4f} {row['compute_time_s']:<15.2f}")

# 与AWS Bedrock价格对比分析
print("\n--- 与AWS Bedrock定价对比 ---")
aws_prices = {
    "llama-4-8b": 0.15,   # AWS Bedrock Llama 4 8B（估算）
    "llama-4-70b": 1.20,
    "llama-4-405b": 8.00,
}
for model_name, meta_price in [(r['model'], r['price_per_million_input_tokens']) 
                                for r in table]:
    aws_price = aws_prices.get(model_name, 0)
    if aws_price > 0:
        discount = (1 - meta_price / aws_price) * 100
        print(f"{model_name}: Meta Compute ${meta_price:.4f} vs AWS ${aws_price:.4f} "
              f"(优惠 {discount:.1f}%)")

package compute

import (
	"context"
	"fmt"
	"sync"
	"time"
)

// Tenant 租户
type Tenant struct {
	ID        string
	Name      string
	Priority  int           // 优先级（Meta内部任务 > 外部租户）
	Quota     ResourceQuota // 资源配额
	Allocated ResourceQuota // 已分配资源
}

// ResourceQuota 资源配额
type ResourceQuota struct {
	GPUCount    int     // GPU数量
	MemoryGB    int     // 显存（GB）
	NetworkBW   float64 // 网络带宽（Gbps）
	StorageGB   int     // 临时存储（GB）
	MaxDuration time.Duration // 最大运行时长
}

// GPUReservation GPU预留
type GPUReservation struct {
	TenantID   string
	GPUIndices []int       // 分配的GPU索引
	StartTime  time.Time
	Duration   time.Duration
	Status     string      // pending / active / preempted / completed
}

// MetaComputeScheduler 多租户算力调度器
type MetaComputeScheduler struct {
	mu            sync.RWMutex
	availableGPUs map[int]bool // GPU索引 → 是否可用
	reservations  []GPUReservation
	tenants       map[string]*Tenant
	preemptQueue  []GPUReservation // 可被抢占的任务队列
}

func NewScheduler(totalGPUs int) *MetaComputeScheduler {
	gpus := make(map[int]bool)
	for i := 0; i < totalGPUs; i++ {
		gpus[i] = true
	}
	return &MetaComputeScheduler{
		availableGPUs: gpus,
		tenants:       make(map[string]*Tenant),
	}
}

// RegisterTenant 注册租户
func (s *MetaComputeScheduler) RegisterTenant(t *Tenant) error {
	s.mu.Lock()
	defer s.mu.Unlock()
	
	if _, exists := s.tenants[t.ID]; exists {
		return fmt.Errorf("tenant %s already exists", t.ID)
	}
	s.tenants[t.ID] = t
	return nil
}

// Schedule 调度GPU资源
func (s *MetaComputeScheduler) Schedule(ctx context.Context, 
	tenantID string, gpuCount int, duration time.Duration) (*GPUReservation, error) {
	
	s.mu.Lock()
	defer s.mu.Unlock()
	
	tenant, ok := s.tenants[tenantID]
	if !ok {
		return nil, fmt.Errorf("unknown tenant: %s", tenantID)
	}
	
	// 检查配额
	if tenant.Allocated.GPUCount+gpuCount > tenant.Quota.GPUCount {
		// 如果超出配额，尝试抢占低优先级任务
		if tenant.Priority >= 5 { // 仅高优先级可触发抢占
			preempted := s.tryPreempt(gpuCount, duration, tenant.Priority)
			if preempted == nil {
				return nil, fmt.Errorf("quota exceeded and no preemptable tasks")
			}
			return preempted, nil
		}
		return nil, fmt.Errorf("quota exceeded for tenant %s", tenantID)
	}
	
	// 查找空闲GPU
	available := s.findAvailableGPUs(gpuCount)
	if len(available) < gpuCount {
		return nil, fmt.Errorf("insufficient GPUs: requested %d, available %d", 
			gpuCount, len(available))
	}
	
	// 预留资源
	for _, idx := range available {
		s.availableGPUs[idx] = false
	}
	
	reservation := GPUReservation{
		TenantID:   tenantID,
		GPUIndices: available,
		StartTime:  time.Now(),
		Duration:   duration,
		Status:     "active",
	}
	s.reservations = append(s.reservations, reservation)
	
	tenant.Allocated.GPUCount += gpuCount
	
	return &reservation, nil
}

// tryPreempt 尝试抢占低优先级任务
func (s *MetaComputeScheduler) tryPreempt(needGPU int, 
	duration time.Duration, priority int) *GPUReservation {
	
	// 查找可抢占的任务（优先级低于当前请求且运行时间较短）
	candidates := make([]int, 0)
	for i, r := range s.reservations {
		if r.Status == "active" {
			t := s.tenants[r.TenantID]
			if t.Priority < priority && time.Since(r.StartTime) > time.Minute*30 {
				candidates = append(candidates, i)
			}
		}
	}
	
	// 收集可释放的GPU
	releasable := make([]int, 0)
	for _, idx := range candidates {
		releasable = append(releasable, s.reservations[idx].GPUIndices...)
		if len(releasable) >= needGPU {
			break
		}
	}
	
	if len(releasable) < needGPU {
		return nil
	}
	
	// 标记被抢占的任务
	for _, idx := range candidates {
		s.reservations[idx].Status = "preempted"
		t := s.tenants[s.reservations[idx].TenantID]
		t.Allocated.GPUCount -= len(s.reservations[idx].GPUIndices)
	}
	
	// 创建新预留
	reservation := GPUReservation{
		TenantID:   "preemptor",
		GPUIndices: releasable[:needGPU],
		StartTime:  time.Now(),
		Duration:   duration,
		Status:     "active",
	}
	
	return &reservation
}

// findAvailableGPUs 查找空闲GPU
func (s *MetaComputeScheduler) findAvailableGPUs(count int) []int {
	result := make([]int, 0)
	for idx, available := range s.availableGPUs {
		if available {
			result = append(result, idx)
			if len(result) >= count {
				break
			}
		}
	}
	return result
}

func main() {
	scheduler := NewScheduler(100000) // 10万卡集群
	
	// 注册Meta内部租户（高优先级）
	metaInternal := &Tenant{
		ID:       "meta-internal",
		Name:     "Meta AI Research",
		Priority: 10,
		Quota:    ResourceQuota{GPUCount: 80000},
	}
	scheduler.RegisterTenant(metaInternal)
	
	// 注册外部租户（低优先级）
	externalTenant := &Tenant{
		ID:       "startup-001",
		Name:     "AI Startup Co.",
		Priority: 3,
		Quota:    ResourceQuota{GPUCount: 5000},
	}
	scheduler.RegisterTenant(externalTenant)
	
	ctx := context.Background()
	
	// 模拟调度
	extRes, err := scheduler.Schedule(ctx, "startup-001", 1024, 2*time.Hour)
	if err != nil {
		fmt.Printf("External schedule error: %v\n", err)
	} else {
		fmt.Printf("External tenant scheduled: %d GPUs\n", len(extRes.GPUIndices))
	}
}

"""
NVIDIA DSX AI Factory 合作模式分析
"""
from enum import Enum
from dataclasses import dataclass

class PartnershipTier(Enum):
    """合作层级"""
    STANDARD = "standard"       # 标准采购
    REVENUE_SHARE = "rev_share" # 收入分成
    CREDIT_BACKED = "credit"    # 信用支持+兜底

@dataclass
class NVIDIA Partnership:
    partner_name: str
    tier: PartnershipTier
    gpu_count: int
    gpu_model: str  # GB300
    upfront_payment_pct: float  # 预付比例
    revenue_share_pct: float    # 收入分成比例（tier相关）
    backstop_pct: float         # NVIDIA兜底比例

class NVIDIAFactoryAnalyzer:
    """NVIDIA AI Factory合作分析器"""
    
    def __init__(self):
        self.partnerships = []
        
    def add_partnership(self, p: NVIDIA Partnership):
        self.partnerships.append(p)
    
    def analyze_economics(self, p: NVIDIA Partnership, 
                          projected_util_rate: float,
                          gpu_price_per_hour: float) -> dict:
        """分析合作的经济模型"""
        
        gpu_cost = 35000  # GB300单卡成本（估算）
        total_hardware_cost = gpu_cost * p.gpu_count
        
        # 预付部分
        upfront = total_hardware_cost * p.upfront_payment_pct
        
        # 年化收入预测（假设80%利用率）
        annual_revenue = (p.gpu_count * gpu_price_per_hour * 
                         24 * 365 * projected_util_rate)
        
        # NVIDIA分成收入
        nvidia_share = annual_revenue * p.revenue_share_pct
        
        # NVIDIA兜底成本（如果利用率低于阈值）
        minimum_commitment = annual_revenue * 0.6  # 60%最低承诺
        if projected_util_rate < 0.6:
            backstop_payment = minimum_commitment - annual_revenue
        else:
            backstop_payment = 0
        
        return {
            "partner": p.partner_name,
            "hardware_cost": total_hardware_cost,
            "upfront_payment": upfront,
            "annual_revenue_projected": annual_revenue,
            "nvidia_revenue_share": nvidia_share,
            "nvidia_backstop_risk": backstop_payment,
            "nvidia_total_annual_return": nvidia_share + backstop_payment * p.backstop_pct
        }

# 分析NVIDIA首批合作伙伴
analyzer = NVIDIAFactoryAnalyzer()

partnerships = [
    NVIDIA Partnership("Sharon AI", PartnershipTier.REVENUE_SHARE, 
                       40000, "GB300", 0.3, 0.15, 0.0),
    NVIDIA Partnership("Firmus (Indonesia)", PartnershipTier.CREDIT_BACKED,
                       170000, "GB300", 0.2, 0.12, 0.8),
]

print("=" * 80)
print("NVIDIA DSX AI Factory 经济模型分析")
print("=" * 80)
for p in partnerships:
    result = analyzer.analyze_economics(p, 0.75, 3.5)
    print(f"\n--- {p.partner_name} ---")
    print(f"GPU规模: {p.gpu_count:,} 张 {p.gpu_model}")
    print(f"硬件总成本: ${result['hardware_cost']/1e6:.1f}M")
    print(f"预付: ${result['upfront_payment']/1e6:.1f}M ({p.upfront_payment_pct*100:.0f}%)")
    print(f"年化预计收入: ${result['annual_revenue_projected']/1e6:.1f}M")
    print(f"NVIDIA年分成: ${result['nvidia_revenue_share']/1e6:.1f}M")
    print(f"NVIDIA兜底风险: ${result['nvidia_backstop_risk']/1e6:.1f}M")