NVIDIA Vera Rubin全面量产深度解析：从Blackwell到Rubin，下一代AI计算平台的工业化里程碑

package main

import (
    "fmt"
    "log"
    "os"
    "cuda" // 假设的CUDA绑定库
)

// RubinGPUConfig 定义Rubin GPU推理配置
type RubinGPUConfig struct {
    DeviceID      int
    PrecisionMode string // NVFP4, FP8, FP16, BF16
    HBM4SizeGB    int
    TensorCores    int
    SMCount        int
    EnableTMA     bool
}

// InferenceBenchmark 推理基准测试结果
type InferenceBenchmark struct {
    ModelName        string
    Precision        string
    BatchSize        int
    ContextLength    int
    TokenPerSec      float64
    MemoryBandwidth  float64 // GB/s
    AchievedUtil     float64 // 百分比
    LatencyMs        float64
}

func NewRubinGPUConfig() *RubinGPUConfig {
    return &RubinGPUConfig{
        DeviceID:      0,
        PrecisionMode: "NVFP4",
        HBM4SizeGB:    288,
        TensorCores:   896,
        SMCount:       224,
        EnableTMA:     true,
    }
}

// RunNVFP4Inference 使用NVFP4精度运行推理
func (c *RubinGPUConfig) RunNVFP4Inference(modelParams int, batchSize int, seqLen int) (*InferenceBenchmark, error) {
    // 配置第三代Transformer Engine自适应精度
    engine := NewTransformerEngineV3()
    engine.SetAdaptiveCompression(true)
    engine.SetTargetPrecision("NVFP4")
    
    // 启用Tensor Memory Accelerator (TMA) 高效数据搬运
    if c.EnableTMA {
        engine.EnableTMA()
    }
    
    // 配置HBM4内存子系统
    memSubsys := NewHBM4Subsystem(c.HBM4SizeGB, 22_000) // 22 TB/s带宽
    memSubsys.AllocateKVCache(seqLen * batchSize * 1024)
    defer memSubsys.Free()
    
    // 加载模型权重（NVFP4量化）
    weights := LoadModelWeightsNVFP4(modelParams, memSubsys)
    
    // 执行推理：使用FP4 Tensor Core
    input := GenerateInputTokens(batchSize, seqLen)
    output := make([]int32, batchSize*seqLen)
    
    // 调用第三代Tensor Core执行矩阵乘法
    // Rubin GPU 在K维度上吞吐量翻倍
    result := engine.ExecuteWithTensorCore(
        weights,
        input,
        output,
        TensorCoreConfig{
            Precision:       "NVFP4",
            KDimensionality: 2, // Rubin的K维度翻倍优化
            UseSparsity:     true,
        },
    )
    
    if result.Error != nil {
        return nil, fmt.Errorf("inference failed: %w", result.Error)
    }
    
    return &InferenceBenchmark{
        ModelName:       fmt.Sprintf("MoE-%dB-NVFP4", modelParams/1e9),
        Precision:       "NVFP4",
        BatchSize:       batchSize,
        ContextLength:   seqLen,
        TokenPerSec:     result.TokenThroughput,
        MemoryBandwidth: result.AchievedBandwidth,
        AchievedUtil:    result.Utilization * 100,
        LatencyMs:       result.LatencyMs,
    }, nil
}

func main() {
    // 模拟在Rubin GPU上运行2T MoE模型
    rubin := NewRubinGPUConfig()
    benchmark, err := rubin.RunNVFP4Inference(2_000_000_000_000, 64, 8192)
    if err != nil {
        log.Fatalf("Benchmark failed: %v", err)
    }
    
    fmt.Printf("=== Rubin GPU NVFP4 推理基准 ===\n")
    fmt.Printf("模型: %s\n", benchmark.ModelName)
    fmt.Printf("精度: %s\n", benchmark.Precision)
    fmt.Printf("Token吞吐量: %.2f tokens/sec\n", benchmark.TokenPerSec)
    fmt.Printf("内存带宽利用率: %.1f%% (%.0f GB/s)\n", 
        benchmark.AchievedUtil, benchmark.MemoryBandwidth)
    fmt.Printf("延迟: %.2f ms\n", benchmark.LatencyMs)
    
    // 与Blackwell FP8对比
    fmt.Printf("\n--- 代际对比 ---\n")
    fmt.Printf("Rubin NVFP4 吞吐量: %.2f tokens/sec\n", benchmark.TokenPerSec)
    fmt.Printf("Blackwell FP8 吞吐量: %.2f tokens/sec\n", benchmark.TokenPerSec/5.0)
    fmt.Printf("提升: %.1fx\n", 5.0) // 对比Blackwell
}

"""
HBM4内存管理模拟：KV Cache分配与MoE模型专家分布
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Tuple

@dataclass
class HBM4Config:
    """每颗Rubin GPU的HBM4配置"""
    capacity_gb: int = 288
    bandwidth_tb_s: float = 22.0
    stacks: int = 12  # 12-Hi
    stack_capacity_gb: int = 24  # 每层24GB
    channels: int = 16
    channel_width_bits: int = 256  # HBM4通道宽度翻倍

@dataclass
class MoEModelConfig:
    """MoE模型配置"""
    total_params_b: int = 2000  # 2T参数
    num_experts: int = 256
    top_k: int = 8
    hidden_dim: int = 16384
    num_layers: int = 96
    kv_head_dim: int = 128
    num_kv_heads: int = 8

class HBM4MemoryManager:
    """HBM4内存管理器 - 模拟Rubin GPU的内存分配策略"""
    
    def __init__(self, config: HBM4Config):
        self.config = config
        self.available = config.capacity_gb * 1024  # 转换为MB
        self.allocations: Dict[str, int] = {}
        
    def estimate_kv_cache_per_layer(self, batch_size: int, seq_len: int, 
                                     kv_dim: int, num_heads: int) -> int:
        """估算单层KV Cache所需内存（MB）"""
        # 每个KV Cache条目 = 2 (K + V) × kv_dim × num_heads × 2 bytes (FP16)
        bytes_per_token = 2 * kv_dim * num_heads * 2  # bytes
        return (batch_size * seq_len * bytes_per_token) / (1024 * 1024)
    
    def plan_expert_distribution(self, model: MoEModelConfig, 
                                  num_gpus: int) -> Dict[int, List[int]]:
        """
        规划MoE专家在GPU间的分布
        Rubin的288GB HBM4可以容纳更多专家本地加载
        """
        expert_size_gb = (model.hidden_dim * model.hidden_dim * 4 * 2) / (1024**3)
        experts_per_gpu = model.num_experts // num_gpus
        
        print(f"每个专家大小: {expert_size_gb:.2f} GB")
        print(f"每GPU承载专家数: {experts_per_gpu}")
        print(f"专家占用内存: {expert_size_gb * experts_per_gpu:.1f} GB")
        print(f"可用内存: {self.config.capacity_gb} GB")
        print(f"专家占用率: {expert_size_gb * experts_per_gpu / self.config.capacity_gb * 100:.1f}%")
        
        # 与Blackwell对比（192GB HBM3e）
        blackwell_capacity = 192
        print(f"\nBlackwell (192GB HBM3e) 专家占用率: "
              f"{expert_size_gb * experts_per_gpu / blackwell_capacity * 100:.1f}%")
        print(f"Rubin额外可用内存: {self.config.capacity_gb - blackwell_capacity} GB")
        
        # 分配专家到各GPU
        distribution = {}
        for gpu_id in range(num_gpus):
            start = gpu_id * experts_per_gpu
            end = start + experts_per_gpu
            distribution[gpu_id] = list(range(start, end))
        
        return distribution
    
    def simulate_long_context_kv_cache(self, 
                                        model: MoEModelConfig,
                                        batch_size: int = 64,
                                        seq_len: int = 131072) -> Tuple[float, float]:
        """
        模拟超长上下文KV Cache占用
        Rubin的288GB HBM4支持更长上下文而无需KV Cache卸载
        """
        # 假设每层使用GQA (Grouped Query Attention)
        kv_dim = model.kv_head_dim
        num_kv_heads = model.num_kv_heads
        
        per_layer_mb = self.estimate_kv_cache_per_layer(
            batch_size, seq_len, kv_dim, num_kv_heads
        )
        total_kv_mb = per_layer_mb * model.num_layers
        total_kv_gb = total_kv_mb / 1024
        
        # 模型权重占用
        weight_gb = model.total_params_b * 2 / 8  # FP16: 2 bytes per param, 8 bits per byte
        weight_gb = weight_gb / (1024 * 1024 * 1024)  # 不准确，简化
        
        # 简化：2T参数FP16约4000GB，需要多GPU分载
        weight_per_gpu_gb = model.total_params_b * 1024 / num_gpus * 2 / 1024 / 1024
        # 更准确的计算：2T × 2 bytes / 1024³ = 约3725 GB
        total_params_bytes = model.total_params_b * 1e9 * 2  # FP16
        weight_per_gpu_gb = total_params_bytes / (1024**3) / num_gpus
        
        total_per_gpu = weight_per_gpu_gb + total_kv_gb
        
        print(f"\n=== 超长上下文推理内存分析 (seq_len={seq_len}) ===")
        print(f"模型权重/GPU: {weight_per_gpu_gb:.1f} GB")
        print(f"KV Cache ({seq_len}上下文): {total_kv_gb:.1f} GB")
        print(f"总计/GPU: {total_per_gpu:.1f} GB")
        print(f"Rubin HBM4容量: {self.config.capacity_gb} GB")
        print(f"内存利用率: {total_per_gpu / self.config.capacity_gb * 100:.1f}%")
        
        if total_per_gpu > self.config.capacity_gb:
            overflow = total_per_gpu - self.config.capacity_gb
            print(f"⚠ 超出 {overflow:.1f} GB，需要KV Cache卸载")
        else:
            remaining = self.config.capacity_gb - total_per_gpu
            print(f"✓ 剩余 {remaining:.1f} GB，完全本地推理")
        
        return weight_per_gpu_gb, total_kv_gb


# 模拟运行
if __name__ == "__main__":
    hbm4 = HBM4Config(capacity_gb=288, bandwidth_tb_s=22.0)
    model = MoEModelConfig()
    num_gpus = 32  # NVL72中的72颗GPU
    
    manager = HBM4MemoryManager(hbm4)
    
    print("=" * 60)
    print("HBM4内存管理模拟 - Vera Rubin NVL72")
    print("=" * 60)
    
    # 1. 专家分布规划
    print("\n[1] MoE专家分布规划")
    distribution = manager.plan_expert_distribution(model, num_gpus)
    
    # 2. 超长上下文KV Cache分析
    print("\n[2] 超长上下文推理分析")
    weight_gb, kv_gb = manager.simulate_long_context_kv_cache(
        model, batch_size=64, seq_len=131072
    )
    
    # 3. 与Blackwell的对比
    print("\n[3] 代际内存对比")
    print(f"{'指标':<30} {'Blackwell (HBM3e)':<20} {'Rubin (HBM4)':<20}")
    print(f"{'-'*30} {'-'*20} {'-'*20}")
    print(f"{'HBM容量':<30} {'192 GB':<20} {'288 GB':<20}")
    print(f"{'内存带宽':<30} {'8 TB/s':<20} {'22 TB/s':<20}")
    print(f"{'带宽提升':<30} {'1x':<20} {'2.8x':<20}")
    print(f"{'KV Cache容量 (131K ctx)':<30} {'~48 GB':<20} {'~48 GB':<20}")
    print(f"{'剩余容量用于模型':<30} {'~144 GB':<20} {'~240 GB':<20}")
    print(f"{'可本地加载模型比例':<30} {'~60%':<20} {'~100%':<20}")

"""
Vera Rubin vs Blackwell 性能基准对比测试代码
"""
import numpy as np
import time
from dataclasses import dataclass, field
from typing import List, Tuple

@dataclass
class BenchmarkResult:
    """基准测试结果"""
    platform: str
    model: str
    tokens_per_sec: float
    power_watts: float
    tokens_per_mw: float  # 每兆瓦Token吞吐量
    latency_p50_ms: float
    latency_p99_ms: float
    cost_per_million_tokens: float
    batch_size: int
    precision: str

class VeraRubinBenchmark:
    """Vera Rubin NVL72 性能基准测试"""
    
    def __init__(self, num_gpus: int = 72):
        self.num_gpus = num_gpus
        self.platform = "Vera Rubin NVL72"
        
        # 每GPU规格
        self.gpu_specs = {
            "hbm4_gb": 288,
            "bandwidth_tb_s": 22.0,
            "nvfp4_pflops": 50,
            "fp8_pflops": 35,
        }
        
        # 机架聚合规格
        self.rack_flops = self.gpu_specs["nvfp4_pflops"] * num_gpus  # 3600 PFLOPS
        self.rack_hbm = self.gpu_specs["hbm4_gb"] * num_gpus / 1024  # 20.7 TB
        
    def benchmark_deepseek_r1(self, batch_size: int = 64, 
                                seq_len: int = 4096) -> BenchmarkResult:
        """
        模拟DeepSeek-R1推理基准测试
        基于CoreWeave公布的实测数据
        """
        # 假设的推理吞吐量（基于10x tokens/mw claim）
        tokens_per_gpu = 2500  # tokens/sec/GPU (NVFP4)
        total_tokens = tokens_per_gpu * self.num_gpus
        
        # 功耗估算
        power_per_gpu = 1200  # 瓦
        rack_power = power_per_gpu * self.num_gpus * 1.15  # 15% overhead
        power_mw = rack_power / 1_000_000  # 转换为兆瓦
        
        # 每兆瓦Token吞吐量
        tokens_per_mw = total_tokens / power_mw
        
        # 延迟估算
        latency_p50 = 35.0  # ms
        latency_p99 = 120.0  # ms
        
        # 成本估算（假设电力成本$0.10/kWh）
        cost_per_hour = rack_power / 1000 * 0.10
        tokens_per_hour = total_tokens * 3600
        cost_per_million = (cost_per_hour / tokens_per_hour) * 1_000_000
        
        return BenchmarkResult(
            platform=self.platform,
            model="DeepSeek-R1",
            tokens_per_sec=total_tokens,
            power_watts=rack_power,
            tokens_per_mw=tokens_per_mw,
            latency_p50_ms=latency_p50,
            latency_p99_ms=latency_p99,
            cost_per_million_tokens=cost_per_million,
            batch_size=batch_size,
            precision="NVFP4"
        )

class BlackwellBenchmark:
    """Blackwell NVL72 性能基准（对比用）"""
    
    def __init__(self, num_gpus: int = 72):
        self.num_gpus = num_gpus
        self.platform = "Grace Blackwell NVL72"
        
        self.gpu_specs = {
            "hbm3e_gb": 192,
            "bandwidth_tb_s": 8.0,
            "fp8_pflops": 10,
        }
        
    def benchmark_deepseek_r1(self, batch_size: int = 64,
                                seq_len: int = 4096) -> BenchmarkResult:
        """Blackwell基准（官方数据的1/10）"""
        tokens_per_gpu = 500  # tokens/sec/GPU (FP8)
        total_tokens = tokens_per_gpu * self.num_gpus
        
        power_per_gpu = 1000  # 瓦
        rack_power = power_per_gpu * self.num_gpus * 1.15
        power_mw = rack_power / 1_000_000
        
        tokens_per_mw = total_tokens / power_mw
        latency_p50 = 85.0
        latency_p99 = 280.0
        
        cost_per_hour = rack_power / 1000 * 0.10
        tokens_per_hour = total_tokens * 3600
        cost_per_million = (cost_per_hour / tokens_per_hour) * 1_000_000
        
        return BenchmarkResult(
            platform=self.platform,
            model="DeepSeek-R1",
            tokens_per_sec=total_tokens,
            power_watts=rack_power,
            tokens_per_mw=tokens_per_mw,
            latency_p50_ms=latency_p50,
            latency_p99_ms=latency_p99,
            cost_per_million_tokens=cost_per_million,
            batch_size=batch_size,
            precision="FP8"
        )

def compare_platforms():
    """对比两个平台"""
    rubin = VeraRubinBenchmark().benchmark_deepseek_r1()
    blackwell = BlackwellBenchmark().benchmark_deepseek_r1()
    
    print("=" * 80)
    print("NVIDIA Vera Rubin NVL72 vs Grace Blackwell NVL72 性能对比")
    print("=" * 80)
    print(f"{'指标':<35} {'Blackwell':<20} {'Rubin':<20} {'提升':<10}")
    print("-" * 85)
    
    metrics = [
        ("Token吞吐量 (tokens/sec)", 
         f"{blackwell.tokens_per_sec:,.0f}", 
         f"{rubin.tokens_per_sec:,.0f}",
         f"{rubin.tokens_per_sec/blackwell.tokens_per_sec:.1f}x"),
        ("每兆瓦Token吞吐量",
         f"{blackwell.tokens_per_mw:,.0f}",
         f"{rubin.tokens_per_mw:,.0f}",
         f"{rubin.tokens_per_mw/blackwell.tokens_per_mw:.1f}x"),
        ("P50延迟 (ms)",
         f"{blackwell.latency_p50_ms:.1f}",
         f"{rubin.latency_p50_ms:.1f}",
         f"{blackwell.latency_p50_ms/rubin.latency_p50_ms:.1f}x"),
        ("P99延迟 (ms)",
         f"{blackwell.latency_p99_ms:.1f}",
         f"{rubin.latency_p99_ms:.1f}",
         f"{blackwell.latency_p99_ms/rubin.latency_p99_ms:.1f}x"),
        ("每百万Token成本 ($)",
         f"${blackwell.cost_per_million_tokens:.4f}",
         f"${rubin.cost_per_million_tokens:.4f}",
         f"{blackwell.cost_per_million_tokens/rubin.cost_per_million_tokens:.1f}x"),
        ("每GPU HBM容量 (GB)",
         f"{BlackwellBenchmark().gpu_specs['hbm3e_gb']}",
         f"{VeraRubinBenchmark().gpu_specs['hbm4_gb']}",
         f"{VeraRubinBenchmark().gpu_specs['hbm4_gb']/BlackwellBenchmark().gpu_specs['hbm3e_gb']:.1f}x"),
        ("内存带宽 (TB/s)",
         f"{BlackwellBenchmark().gpu_specs['bandwidth_tb_s']}",
         f"{VeraRubinBenchmark().gpu_specs['bandwidth_tb_s']}",
         f"{VeraRubinBenchmark().gpu_specs['bandwidth_tb_s']/BlackwellBenchmark().gpu_specs['bandwidth_tb_s']:.1f}x"),
    ]
    
    for name, b_val, r_val, uplift in metrics:
        print(f"{name:<35} {b_val:<20} {r_val:<20} {uplift:<10}")
    
    print("-" * 85)
    print(f"\n结论: Rubin在Token吞吐量、延迟、成本和能效方面全面领先。")
    print(f"每兆瓦Token吞吐量提升{rubin.tokens_per_mw/blackwell.tokens_per_mw:.0f}x，")
    print(f"意味着同等电力预算下可处理10倍的推理负载。")

if __name__ == "__main__":
    compare_platforms()