Zhipu 1GW Domestic Chip AI Data Center + Microsoft Mistral Multi-Billion Dollar Europe AI Infrastructure: The Global Restructuring of the Compute Arms Race

import asyncio
import time
import json
from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import random


class ChipType(Enum):
    ASCEND_910B = "ascend_910b"
    ASCEND_950 = "ascend_950"
    CAMBRICON_SG = "cambricon_sg"
    BA_3000 = "ba_3000"


@dataclass
class ChipSpec:
    chip_type: ChipType
    tflops_fp16: float
    hbm_size_gb: int
    memory_bandwidth_gbps: float
    power_watts: int


@dataclass
class ComputeTask:
    task_id: str
    model_name: str
    model_size_b: float
    batch_size: int
    seq_length: int
    precision: str
    priority: int = 0
    submitted_at: float = field(default_factory=time.time)
    status: str = "pending"


class HeterogeneousScheduler:
    """
    Heterogeneous compute scheduler
    Supports collaborative scheduling across multiple domestic chip vendors
    """
    def __init__(self):
        self.chip_pools: Dict[ChipType, list] = {}
        self.task_queue: asyncio.Queue = asyncio.Queue()
        self.running_tasks: Dict[str, ComputeTask] = {}
        self.completed_tasks: List[ComputeTask] = []

        self.chip_specs: Dict[ChipType, ChipSpec] = {
            ChipType.ASCEND_910B: ChipSpec(
                ChipType.ASCEND_910B, 320, 96, 1800, 310),
            ChipType.ASCEND_950: ChipSpec(
                ChipType.ASCEND_950, 520, 192, 2800, 450),
            ChipType.CAMBRICON_SG: ChipSpec(
                ChipType.CAMBRICON_SG, 256, 64, 1200, 250),
            ChipType.BA_3000: ChipSpec(
                ChipType.BA_3000, 400, 128, 2000, 350),
        }

        self.stats = {"total_tasks": 0, "total_time": 0.0, "chip_usage": {}}

    def register_chip_pool(self, chip_type: ChipType, count: int):
        if chip_type not in self.chip_pools:
            self.chip_pools[chip_type] = []
        for i in range(count):
            self.chip_pools[chip_type].append({
                "id": f"{chip_type.value}_{i:04d}",
                "chip_type": chip_type,
                "status": "idle"
            })
        print(f"Registered {chip_type.value}: {count} chips")

    def estimate_task_cost(self, task: ComputeTask, chip_type: ChipType) -> float:
        spec = self.chip_specs[chip_type]
        compute_ops = task.model_size_b * 1e9 * task.seq_length * task.batch_size * 8
        effective_tflops = spec.tflops_fp16 * 0.6
        compute_time = compute_ops / (effective_tflops * 1e12)
        return compute_time

    def select_optimal_chip(self, task: ComputeTask) -> Tuple[Optional[ChipType], float]:
        best_chip = None
        best_time = float('inf')
        for chip_type, pool in self.chip_pools.items():
            available = sum(1 for n in pool if n["status"] == "idle")
            if available == 0:
                continue
            estimated_time = self.estimate_task_cost(task, chip_type)
            if estimated_time < best_time:
                best_time = estimated_time
                best_chip = chip_type
        return best_chip, best_time

    async def schedule_task(self, task: ComputeTask) -> str:
        chip_type, estimated_time = self.select_optimal_chip(task)
        if chip_type is None:
            task.status = "queued"
            await self.task_queue.put(task)
            return "queued"

        # Allocate node
        pool = self.chip_pools.get(chip_type, [])
        node = next((n for n in pool if n["status"] == "idle"), None)
        if node is None:
            task.status = "queued"
            await self.task_queue.put(task)
            return "queued"

        node["status"] = "busy"
        task.status = "running"
        self.running_tasks[task.task_id] = task

        execution_time = estimated_time * random.uniform(0.8, 1.2)
        await asyncio.sleep(min(execution_time * 0.001, 0.01))  # Simulated

        node["status"] = "idle"
        task.status = "completed"
        self.stats["total_tasks"] += 1
        self.stats["total_time"] += execution_time
        self.stats["chip_usage"][chip_type] = self.stats["chip_usage"].get(chip_type, 0) + 1
        self.completed_tasks.append(task)
        del self.running_tasks[task.task_id]
        return "completed"

    def get_cluster_stats(self) -> Dict:
        total_chips = sum(len(pool) for pool in self.chip_pools.values())
        busy_chips = sum(sum(1 for n in pool if n["status"] == "busy")
                        for pool in self.chip_pools.values())
        total_power = sum(
            len(pool) * self.chip_specs[list(self.chip_pools.keys())[0]].power_watts / 1e6
            for pool in self.chip_pools.values()
        )
        return {
            "total_chips": total_chips,
            "busy_chips": busy_chips,
            "idle_chips": total_chips - busy_chips,
            "total_tasks": self.stats["total_tasks"],
            "total_power_mw": total_power
        }


async def simulate_10k_cluster():
    scheduler = HeterogeneousScheduler()
    pools = {
        ChipType.ASCEND_910B: 3000,
        ChipType.ASCEND_950: 2000,
        ChipType.CAMBRICON_SG: 3000,
        ChipType.BA_3000: 2000,
    }
    for chip_type, count in pools.items():
        scheduler.register_chip_pool(chip_type, count)

    tasks = []
    for i in range(100):
        task = ComputeTask(
            task_id=f"task_{i:04d}",
            model_name=random.choice(["GLM-5.2", "GPT-5.6", "Qwen-3.8"]),
            model_size_b=random.choice([100, 175, 285, 500]),
            batch_size=random.randint(1, 64),
            seq_length=random.choice([4096, 8192, 16384]),
            precision=random.choice(["fp16", "int8"])
        )
        tasks.append(task)

    results = await asyncio.gather(*[scheduler.schedule_task(t) for t in tasks])
    stats = scheduler.get_cluster_stats()

    print("Zhipu 1GW Heterogeneous Cluster Simulation:")
    print(f"  Total chips: {stats['total_chips']}")
    print(f"  Power consumption: {stats['total_power_mw']:.2f} MW")
    print(f"  Total tasks: {stats['total_tasks']}")
    print(f"  Busy: {stats['busy_chips']}, Idle: {stats['idle_chips']}")
    return stats


if __name__ == "__main__":
    import numpy as np
    asyncio.run(simulate_10k_cluster())

package main

import (
	"fmt"
	"math"
	"sort"
	"sync"
	"time"
)

type Region string

const (
	RegionChina  Region = "china"
	RegionEurope Region = "europe"
	RegionUSWest Region = "us_west"
)

type RegionConstraint struct {
	Region          Region
	DataMustStay    bool
	LatencyMs       int
	ComplianceLevel string
	CapacityTFLOPS  float64
}

type GlobalInfrastructureOrchestrator struct {
	mu         sync.RWMutex
	regions    map[Region]*RegionalCluster
	routeTable map[Region]map[Region]int
}

type RegionalCluster struct {
	Region        Region
	TotalGPUs     int
	AvailableGPUs int
	TotalPowerMW  float64
	ModelCache    map[string]bool
	LastSyncTime  time.Time
}

func NewGlobalInfrastructureOrchestrator() *GlobalInfrastructureOrchestrator {
	return &GlobalInfrastructureOrchestrator{
		regions: make(map[Region]*RegionalCluster),
		routeTable: map[Region]map[Region]int{
			RegionChina:  {RegionChina: 5, RegionEurope: 150, RegionUSWest: 120},
			RegionEurope: {RegionChina: 150, RegionEurope: 5, RegionUSWest: 80},
			RegionUSWest: {RegionChina: 120, RegionEurope: 80, RegionUSWest: 5},
		},
	}
}

func (o *GlobalInfrastructureOrchestrator) AddRegionalCluster(region Region,
	gpus int, powerMW float64) {
	o.mu.Lock()
	defer o.mu.Unlock()
	o.regions[region] = &RegionalCluster{
		Region:        region,
		TotalGPUs:     gpus,
		AvailableGPUs: gpus,
		TotalPowerMW:  powerMW,
		ModelCache:    make(map[string]bool),
		LastSyncTime:  time.Now(),
	}
	fmt.Printf("[Infra] Added region %s: %d GPUs, %.1f MW\n", region, gpus, powerMW)
}

func (o *GlobalInfrastructureOrchestrator) ScheduleInference(
	modelName string, originRegion Region, dataSensitivity string,
	latencyBudgetMs int) (*RegionalCluster, error) {

	o.mu.RLock()
	defer o.mu.RUnlock()

	var candidates []*RegionalCluster
	for _, cluster := range o.regions {
		latency := o.routeTable[originRegion][cluster.Region]
		if latency > latencyBudgetMs {
			continue
		}
		if cluster.AvailableGPUs < 1 {
			continue
		}
		candidates = append(candidates, cluster)
	}

	if len(candidates) == 0 {
		return nil, fmt.Errorf("no suitable region found")
	}

	sort.Slice(candidates, func(i, j int) bool {
		scoreI := float64(candidates[i].AvailableGPUs)/float64(candidates[i].TotalGPUs) +
			math.Max(0, 1.0-float64(o.routeTable[originRegion][candidates[i].Region])/float64(latencyBudgetMs))
		scoreJ := float64(candidates[j].AvailableGPUs)/float64(candidates[j].TotalGPUs) +
			math.Max(0, 1.0-float64(o.routeTable[originRegion][candidates[j].Region])/float64(latencyBudgetMs))
		return scoreI > scoreJ
	})

	selected := candidates[0]
	selected.AvailableGPUs--
	return selected, nil
}

func main() {
	orch := NewGlobalInfrastructureOrchestrator()
	orch.AddRegionalCluster(RegionChina, 50000, 1000)
	orch.AddRegionalCluster(RegionEurope, 20000, 400)
	orch.AddRegionalCluster(RegionUSWest, 30000, 600)

	scenarios := []struct {
		origin     Region
		sensitivity string
		latencyMs  int
	}{
		{RegionEurope, "regulated", 50},
		{RegionChina, "internal", 50},
		{RegionUSWest, "sensitive", 100},
	}

	fmt.Println("\nInference Scheduling Results:")
	for _, s := range scenarios {
		cluster, err := orch.ScheduleInference("GLM-5.2", s.origin, s.sensitivity, s.latencyMs)
		if err != nil {
			fmt.Printf("  %s: Failed - %v\n", s.origin, err)
		} else {
			fmt.Printf("  %s -> Selected %s (%d/%d GPUs available)\n",
				s.origin, cluster.Region, cluster.AvailableGPUs, cluster.TotalGPUs)
		}
	}
}