DeepSeek and Zhipu AI Enter the Chip Arena: The Paradigm Shift of Chinese LLM Companies from Algorithm to Full-Stack Inference Silicon

"""
DeepSeek & Zhipu AI Custom Inference Chip: Cost-Benefit Analysis Framework

This module provides a comprehensive simulation of the economic trade-offs
between purchasing NVIDIA GPUs and developing custom inference ASICs.
"""

from dataclasses import dataclass, field
from typing import List, Dict, Optional
import math


@dataclass
class ChipConfig:
    """Hardware configuration for cost analysis"""
    name: str
    unit_cost_usd: float          # Per-chip or per-card cost
    power_watts: float            # Thermal design power
    throughput_tokens_per_sec: int  # Inference throughput
    price_per_million_tokens: float  # Service cost / token
    lifespan_years: int = 4
    deployment_scale: int = 1      # Number of chips deployed


@dataclass
class ASICProject:
    """Custom ASIC project parameters"""
    rnd_cost_usd: float = 500_000_000       # 5-year R&D budget
    tapeout_cost_usd: float = 30_000_000     # Per mask set (N5)
    design_cycle_years: float = 2.5          # Architecture to tapeout
    software_ecosystem_cost: float = 200_000_000  # Compiler + runtime + SDK
    annual_maintenance: float = 50_000_000   # Post-tapeout engineering


def compute_break_even(
    nvidia: ChipConfig,
    asic: ChipConfig,
    asic_project: ASICProject,
    daily_tokens: int,
    projection_years: int = 5
) -> Dict:
    """
    Compute break-even analysis between NVIDIA GPU and custom ASIC.

    The analysis models:
    - Total Cost of Ownership (TCO) for both approaches
    - Cumulative savings over projection period
    - Break-even point in years
    - Sensitivity to daily token volume
    """
    days_per_year = 365
    annual_tokens = daily_tokens * days_per_year

    # GPU model: pure opex (pay per token)
    gpu_annual_tokens_cost = (annual_tokens / 1_000_000) * nvidia.price_per_million_tokens
    gpu_hardware_annual = nvidia.unit_cost_usd / nvidia.lifespan_years * nvidia.deployment_scale
    gpu_power_annual = nvidia.power_watts * 24 * 365 * 0.12 / 1000 * nvidia.deployment_scale
    gpu_total_annual = gpu_annual_tokens_cost + gpu_hardware_annual + gpu_power_annual

    # ASIC model: upfront capex + lower opex
    total_asic_capex = (
        asic_project.rnd_cost_usd +
        asic_project.tapeout_cost_usd +
        asic_project.software_ecosystem_cost
    )
    asic_annual_tokens_cost = (annual_tokens / 1_000_000) * asic.price_per_million_tokens
    asic_hardware_annual = asic.unit_cost_usd / asic.lifespan_years * asic.deployment_scale
    asic_power_annual = asic.power_watts * 24 * 365 * 0.12 / 1000 * asic.deployment_scale
    asic_annual_maintenance = asic_project.annual_maintenance
    asic_annual_opex = (
        asic_annual_tokens_cost +
        asic_hardware_annual +
        asic_power_annual +
        asic_annual_maintenance
    )

    # Build cumulative cost projection
    timeline = []
    for year in range(1, projection_years + 1):
        if year <= asic_project.design_cycle_years:
            # During design phase: only capex incurred, no opex savings yet
            asic_yearly = total_asic_capex / asic_project.design_cycle_years
            gpu_yearly = gpu_total_annual
        else:
            # Post-deployment: full opex savings
            asic_yearly = asic_annual_opex
            gpu_yearly = gpu_total_annual

        cumulative_gpu = gpu_yearly * year
        cumulative_asic = asic_yearly * year
        savings = cumulative_gpu - cumulative_asic

        timeline.append({
            "year": year,
            "gpu_annual": round(gpu_yearly, 2),
            "asic_annual": round(asic_yearly, 2),
            "cumulative_gpu": round(cumulative_gpu, 2),
            "cumulative_asic": round(cumulative_asic, 2),
            "cumulative_savings": round(savings, 2),
            "break_even_reached": savings >= 0,
        })

    break_even_year = next(
        (y["year"] for y in timeline if y["break_even_reached"]),
        None
    )

    return {
        "daily_tokens": daily_tokens,
        "annual_tokens": annual_tokens,
        "timeline": timeline,
        "break_even_year": break_even_year,
        "total_asic_capex": total_asic_capex,
    }


def sensitivity_analysis() -> None:
    """
    Run sensitivity analysis across different token volume scenarios
    for DeepSeek and Zhipu AI scale operations.
    """
    nvidia_gpu = ChipConfig(
        name="NVIDIA H100",
        unit_cost_usd=35000,
        power_watts=700,
        throughput_tokens_per_sec=1000,
        price_per_million_tokens=2.0,
        deployment_scale=10000,
    )

    custom_asic = ChipConfig(
        name="Custom Inference ASIC",
        unit_cost_usd=8000,
        power_watts=250,
        throughput_tokens_per_sec=2000,
        price_per_million_tokens=0.8,
        deployment_scale=10000,
    )

    asic_project = ASICProject()

    # Three scenarios: DeepSeek scale, Zhipu scale, and combined
    scenarios = [
        ("DeepSeek Scale (10B tokens/day)", 10_000_000_000),
        ("Zhipu GLM-5.2 Scale (5B tokens/day)", 5_000_000_000),
        ("Combined Scale (20B tokens/day)", 20_000_000_000),
    ]

    print("=" * 80)
    print("Custom Inference ASIC: Sensitivity Analysis")
    print("=" * 80)
    print(f"\nBaseline Parameters:")
    print(f"  NVIDIA GPU: ${nvidia_gpu.price_per_million_tokens}/M tokens")
    print(f"  Custom ASIC: ${custom_asic.price_per_million_tokens}/M tokens")
    print(f"  Cost Reduction: "
          f"{(1 - custom_asic.price_per_million_tokens / nvidia_gpu.price_per_million_tokens)*100:.1f}%")
    print(f"  ASIC R&D Budget: ${asic_project.rnd_cost_usd:,.0f}")
    print(f"  Software Ecosystem: ${asic_project.software_ecosystem_cost:,.0f}")
    print(f"  Total Capex: ${asic_project.rnd_cost_usd + asic_project.tapeout_cost_usd + asic_project.software_ecosystem_cost:,.0f}")

    for scenario_name, daily_tokens in scenarios:
        result = compute_break_even(
            nvidia=nvidia_gpu,
            asic=custom_asic,
            asic_project=asic_project,
            daily_tokens=daily_tokens,
        )
        print(f"\n{'─' * 80}")
        print(f"Scenario: {scenario_name}")
        print(f"  Daily Tokens: {result['daily_tokens']:,}")
        print(f"  Annual Tokens: {result['annual_tokens']:,}")
        print(f"  Total ASIC Capex: ${result['total_asic_capex']:,.0f}")
        print(f"  Break-Even Year: "
              f"{'Year ' + str(result['break_even_year']) if result['break_even_year'] else 'Not reached in 5 years'}")

        for yr in result["timeline"]:
            status = "✅ BREAK-EVEN" if yr["break_even_reached"] else "❌"
            print(f"    Year {yr['year']}: "
                  f"GPU=${yr['cumulative_gpu']:>12,.0f} | "
                  f"ASIC=${yr['cumulative_asic']:>12,.0f} | "
                  f"Savings=${yr['cumulative_savings']:>+12,.0f} {status}")

    # Per-token cost comparison
    print(f"\n{'=' * 80}")
    print("Per-Token Cost Comparison")
    print(f"{'=' * 80}")
    print(f"  NVIDIA H100 (10K cluster):  ${nvidia_gpu.price_per_million_tokens}/M tokens")
    print(f"  Custom ASIC (10K cluster):   ${custom_asic.price_per_million_tokens}/M tokens")
    print(f"  Savings:                     "
          f"${nvidia_gpu.price_per_million_tokens - custom_asic.price_per_million_tokens:.2f}/M tokens "
          f"({(1-custom_asic.price_per_million_tokens/nvidia_gpu.price_per_million_tokens)*100:.0f}%)")
    print(f"  Annual Savings at 10B/day:   "
          f"${(10_000_000_000 / 1_000_000) * (nvidia_gpu.price_per_million_tokens - custom_asic.price_per_million_tokens) * 365:,.0f}")


if __name__ == "__main__":
    sensitivity_analysis()

// Go implementation: Token-level cost accounting for inference clusters
package main

import (
    "fmt"
    "math"
)

// TokenCostModel provides per-token cost breakdown
type TokenCostModel struct {
    GPUModel          string
    GPUUnitCost       float64 // USD
    GPUUnitPower      float64 // Watts
    GPUThroughput     float64 // tokens/sec
    GPUPricePerMTok   float64 // USD
    GPULifespan       float64 // years
    ClusterSize       int
    UtilizationRate   float64 // 0.0-1.0
    ElectricityPrice  float64 // USD/kWh
}

func (m *TokenCostModel) AnnualHardwareCost() float64 {
    totalGPU := float64(m.ClusterSize) * m.GPUUnitCost
    return totalGPU / m.GPULifespan
}

func (m *TokenCostModel) AnnualPowerCost() float64 {
    annualKWh := float64(m.ClusterSize) * m.GPUUnitPower * 24 * 365 / 1000
    return annualKWh * m.ElectricityPrice
}

func (m *TokenCostModel) AnnualThroughputTokens() float64 {
    // Effective throughput accounting for utilization
    effectiveTPS := m.GPUThroughput * m.UtilizationRate
    return effectiveTPS * 60 * 60 * 24 * 365 * float64(m.ClusterSize)
}

func (m *TokenCostModel) CostPerToken() float64 {
    totalCost := m.AnnualHardwareCost() + m.AnnualPowerCost()
    annualTokens := m.AnnualThroughputTokens()
    if annualTokens == 0 {
        return math.Inf(1)
    }
    return totalCost / annualTokens
}

func (m *TokenCostModel) CostPerMillionTokens() float64 {
    return m.CostPerToken() * 1_000_000
}

type ASICCostModel struct {
    ChipUnitCost      float64
    ChipUnitPower     float64
    ChipThroughput    float64
    ChipLifespan      float64
    RNDCost           float64
    SoftwareCost      float64
    ClusterSize       int
    UtilizationRate   float64
    ElectricityPrice  float64
}

func (a *ASICCostModel) AnnualAmortizedCapex() float64 {
    totalCapex := a.RNDCost + a.SoftwareCost
    chipHardware := float64(a.ClusterSize) * a.ChipUnitCost
    return (totalCapex / 5.0) + (chipHardware / a.ChipLifespan)
}

func (a *ASICCostModel) AnnualPowerCost() float64 {
    annualKWh := float64(a.ClusterSize) * a.ChipUnitPower * 24 * 365 / 1000
    return annualKWh * a.ElectricityPrice
}

func (a *ASICCostModel) AnnualThroughputTokens() float64 {
    effectiveTPS := a.ChipThroughput * a.UtilizationRate
    return effectiveTPS * 60 * 60 * 24 * 365 * float64(a.ClusterSize)
}

func (a *ASICCostModel) CostPerMillionTokens() float64 {
    totalCost := a.AnnualAmortizedCapex() + a.AnnualPowerCost()
    annualTokens := a.AnnualThroughputTokens()
    if annualTokens == 0 {
        return math.Inf(1)
    }
    return (totalCost / annualTokens) * 1_000_000
}

func main() {
    // NVIDIA H100 cluster: 10,000 GPUs
    nvidia := TokenCostModel{
        GPUModel:         "NVIDIA H100",
        GPUUnitCost:      35000,
        GPUUnitPower:     700,
        GPUThroughput:    1000,
        GPUPricePerMTok:  2.0,
        GPULifespan:      4,
        ClusterSize:      10000,
        UtilizationRate:  0.85,
        ElectricityPrice: 0.12,
    }

    // Custom ASIC: 10,000 chips
    asic := ASICCostModel{
        ChipUnitCost:     8000,
        ChipUnitPower:    250,
        ChipThroughput:   2000,
        ChipLifespan:     4,
        RNDCost:          500_000_000,
        SoftwareCost:     200_000_000,
        ClusterSize:      10000,
        UtilizationRate:  0.90,
        ElectricityPrice: 0.12,
    }

    fmt.Println("=" + strings.Repeat("=", 69))
    fmt.Println("Token-Level Cost Accounting: GPU vs Custom ASIC")
    fmt.Println("=" + strings.Repeat("=", 69))

    fmt.Printf("\nNVIDIA H100 Cluster (10,000 GPUs):\n")
    fmt.Printf("  Annual HW Depreciation: $%.2f M\n", nvidia.AnnualHardwareCost()/1e6)
    fmt.Printf("  Annual Power Cost:     $%.2f M\n", nvidia.AnnualPowerCost()/1e6)
    fmt.Printf("  Annual Throughput:     %.2e tokens\n", nvidia.AnnualThroughputTokens())
    fmt.Printf("  Cost/Token:            $%.2e\n", nvidia.CostPerToken())
    fmt.Printf("  Cost/M Tokens:         $%.2f\n", nvidia.CostPerMillionTokens())

    fmt.Printf("\nCustom Inference ASIC (10,000 chips):\n")
    fmt.Printf("  Annual Amortized Capex: $%.2f M\n", asic.AnnualAmortizedCapex()/1e6)
    fmt.Printf("  Annual Power Cost:      $%.2f M\n", asic.AnnualPowerCost()/1e6)
    fmt.Printf("  Annual Throughput:      %.2e tokens\n", asic.AnnualThroughputTokens())
    fmt.Printf("  Cost/M Tokens:          $%.2f\n", asic.CostPerMillionTokens())

    savings := nvidia.CostPerMillionTokens() - asic.CostPerMillionTokens()
    pct := savings / nvidia.CostPerMillionTokens() * 100
    fmt.Printf("\n%s\n", strings.Repeat("─", 70))
    fmt.Printf("Savings per Million Tokens: $%.2f (%.1f%%)\n", savings, pct)

    // Project 5-year cumulative savings for DeepSeek scale
    dailyTokens := 10_000_000_000.0
    annualTokens := dailyTokens * 365
    annualGpuCost := (annualTokens / 1_000_000) * nvidia.CostPerMillionTokens()
    annualAsicCost := (annualTokens / 1_000_000) * asic.CostPerMillionTokens()
    fmt.Printf("\n5-Year Projection (10B tokens/day):\n")
    fmt.Printf("  GPU Total Cost:     $%.2f B\n", annualGpuCost*5/1e9)
    fmt.Printf("  ASIC Total Cost:    $%.2f B\n", annualAsicCost*5/1e9)
    fmt.Printf("  Net Savings:        $%.2f B\n", (annualGpuCost-annualAsicCost)*5/1e9)
}

"""
DeepSeek UE8M0 FP8 Data Format: Technical Analysis

The UE8M0 format is a specialized FP8 variant designed for inference
optimization. Understanding its hardware implications is crucial for
assessing DeepSeek's algorithm-to-silicon strategy.
"""

import numpy as np
from enum import Enum
from typing import Tuple, Optional


class FP8Format(Enum):
    """FP8 format variants"""
    E4M3 = "E4M3"   # 4 exponent bits, 3 mantissa bits (range: ±448)
    E5M2 = "E5M2"   # 5 exponent bits, 2 mantissa bits (range: ±57344)
    E8M0 = "E8M0"   # 8 exponent bits, 0 mantissa bits (power-of-2 quanta)
    UE8M0 = "UE8M0" # Unsigned E8M0 (no sign bit, 0-2^255 range)


class FP8Converter:
    """
    FP8 format converter with hardware cost modeling.
    Evaluates the trade-off between precision and hardware efficiency.
    """
    
    def __init__(self, format_type: FP8Format):
        self.format = format_type
    
    def quantize(self, values: np.ndarray) -> Tuple[np.ndarray, float]:
        """
        Quantize float32 values to the specified FP8 format.
        Returns (quantized_values, quantization_error).
        """
        if self.format == FP8Format.E4M3:
            max_val = 448.0
            quantized = np.clip(values, -max_val, max_val)
            scale = max_val / 127.0
            quantized = np.round(quantized / scale) * scale
        elif self.format == FP8Format.E5M2:
            max_val = 57344.0
            quantized = np.clip(values, -max_val, max_val)
            scale = max_val / 127.0
            quantized = np.round(quantized / scale) * scale
        elif self.format == FP8Format.UE8M0:
            # Unsigned power-of-2 representation
            # Values are quantized to nearest power of 2
            signs = np.sign(values)
            abs_vals = np.abs(values)
            # Find nearest power of 2
            powers = np.round(np.log2(np.maximum(abs_vals, 1e-10)))
            quantized = signs * (2.0 ** powers)
            quantized = np.maximum(quantized, 0)  # Unsigned
        elif self.format == FP8Format.UE8M0:
            # Same as UE8M0 above (UE8M0 = unsigned E8M0)
            abs_vals = np.maximum(values, 0)
            powers = np.round(np.log2(np.maximum(abs_vals, 1e-10)))
            quantized = 2.0 ** powers
            quantized = np.clip(quantized, 0, 2**255)  # 8-bit exponent max
        else:
            raise ValueError(f"Unknown format: {self.format}")
        
        mse = float(np.mean((values - quantized) ** 2))
        return quantized, mse
    
    def hardware_cost_factor(self) -> float:
        """
        Estimate relative hardware implementation cost.
        Lower values = cheaper to implement in silicon.
        E4M3 (baseline=1.0): most complex (sign + 4E + 3M)
        E5M2: slightly simpler (sign + 5E + 2M)
        E8M0: much simpler (sign + 8E, no mantissa)
        UE8M0: simplest (no sign, 8E, no mantissa)
        """
        cost_map = {
            FP8Format.E4M3: 1.0,
            FP8Format.E5M2: 0.85,
            FP8Format.E8M0: 0.55,
            FP8Format.UE8M0: 0.40,
        }
        return cost_map.get(self.format, 1.0)
    
    def memory_savings_vs_fp16(self) -> float:
        """Memory savings compared to FP16 (16-bit)"""
        return 1.0 - (8.0 / 16.0)  # Always 50% for any FP8 variant


def compare_formats() -> None:
    """Compare all FP8 formats on representative transformer weights"""
    np.random.seed(42)
    
    # Simulate a transformer layer's weight distribution
    # Attention weights tend to be centered around 0 with heavy tails
    n_weights = 1_000_000
    weights = np.random.normal(0, 0.5, n_weights)
    # Add some outlier values
    weights[np.random.choice(n_weights, 1000)] = np.random.uniform(10, 50, 1000)
    
    print("=" * 72)
    print("FP8 Format Comparison for Transformer Weights")
    print("=" * 72)
    
    formats = [FP8Format.E4M3, FP8Format.E5M2, FP8Format.UE8M0]
    
    for fmt in formats:
        converter = FP8Converter(fmt)
        quantized, mse = converter.quantize(weights)
        
        # Compute signal-to-noise ratio
        signal_power = np.mean(weights ** 2)
        snr_db = 10 * np.log10(signal_power / max(mse, 1e-20))
        
        print(f"\n{fmt.value}:")
        print(f"  Bits: 1 sign + {fmt.value[1]}E + {fmt.value[2]}M"
              if fmt != FP8Format.UE8M0 else "  Bits: 0 sign + 8E + 0M (unsigned)")
        print(f"  Range: {quantized.min():.2f} to {quantized.max():.2f}")
        print(f"  MSE:                    {mse:.6e}")
        print(f"  SNR:                    {snr_db:.2f} dB")
        print(f"  Hardware Cost Factor:   {converter.hardware_cost_factor():.2f}x")
        print(f"  Memory vs FP16:         {converter.memory_savings_vs_fp16()*100:.0f}%")
        print(f"  Unique Values:          {len(np.unique(quantized))}")


class HardwareEfficiencyModel:
    """
    Model the hardware-level efficiency of different FP8 implementations.
    This is critical for understanding why UE8M0 is ideal for custom ASIC.
    """
    
    @staticmethod
    def compute_costs() -> None:
        """
        Compute the silicon area and power implications of each format.
        Based on typical MAC (multiply-accumulate) unit design.
        """
        # Reference: FP16 MAC unit cost
        fp16_area = 1.0   # normalized
        fp16_power = 1.0  # normalized
        
        configs = [
            ("FP16", 16, 1, 5, 10, 1.0, 1.0, "Baseline"),
            ("E4M3", 8, 1, 4, 3, 0.55, 0.50, "Standard FP8"),
            ("E5M2", 8, 1, 5, 2, 0.50, 0.45, "Wider range FP8"),
            ("E8M0", 8, 1, 8, 0, 0.30, 0.25, "No mantissa logic"),
            ("UE8M0", 8, 0, 8, 0, 0.22, 0.18, "No sign, no mantissa"),
        ]
        
        print(f"\n{'=' * 72}")
        print("Hardware Efficiency Analysis: MAC Unit Cost")
        print(f"{'=' * 72}")
        print(f"{'Format':<8} {'Bits':<6} {'Sign':<6} {'Exp':<6} {'Mant':<6} "
              f"{'Rel Area':<10} {'Rel Power':<10} Note")
        print(f"{'-'*72}")
        
        for name, bits, sign, exp, mant, area, power, note in configs:
            print(f"{name:<8} {bits:<6} {sign:<6} {exp:<6} {mant:<6} "
                  f"{area:<10.2f} {power:<10.2f} {note}")


if __name__ == "__main__":
    compare_formats()
    HardwareEfficiencyModel.compute_costs()

"""
Process Node Comparison: Impact on Chip Design Parameters
"""

def process_node_analysis():
    """Compare accessible vs cutting-edge process nodes for AI chips"""
    nodes = {
        "TSMC N3 (Cutting edge)": {
            "transistor_density": 310,  # MTr/mm²
            "power_reduction": 0.65,    # vs N5
            "clock_speed": 3.0,         # GHz
            "yield_rate": 0.75,
            "wafer_cost": 20000,        # USD
            "accessible_by_cn": False,
        },
        "TSMC N5 (Current gen)": {
            "transistor_density": 170,
            "power_reduction": 1.0,
            "clock_speed": 2.5,
            "yield_rate": 0.90,
            "wafer_cost": 16000,
            "accessible_by_cn": False,
        },
        "SMIC N+2 (Domestic best)": {
            "transistor_density": 90,
            "power_reduction": 1.3,
            "clock_speed": 2.0,
            "yield_rate": 0.60,
            "wafer_cost": 8000,
            "accessible_by_cn": True,
        },
        "SMIC N+1 (Domestic roadmap)": {
            "transistor_density": 130,
            "power_reduction": 1.1,
            "clock_speed": 2.2,
            "yield_rate": 0.50,
            "wafer_cost": 10000,
            "accessible_by_cn": True,
        },
    }
    
    print(f"{'=' * 80}")
    print(f"Process Node Comparison for AI Inference Chip")
    print(f"{'=' * 80}")
    print(f"{'Node':<28} {'Density':<10} {'Power':<8} {'Clock':<8} "
          f"{'Yield':<8} {'Cost':<10} {'CN Access'}")
    print(f"{'-' * 80}")
    
    for name, spec in nodes.items():
        density = f"{spec['transistor_density']}M/mm²"
        power = f"{spec['power_reduction']:.1f}x"
        clock = f"{spec['clock_speed']}GHz"
        yield_ = f"{spec['yield_rate']*100:.0f}%"
        cost = f"${spec['wafer_cost']:,}"
        access = "✅" if spec['accessible_by_cn'] else "❌"
        print(f"{name:<28} {density:<10} {power:<8} {clock:<8} "
              f"{yield_:<8} {cost:<10} {access}")
    
    # Compute architectural compensation factor
    print(f"\n{'─' * 80}")
    print("Architectural Compensation Analysis")
    print(f"To compete with TSMC N3 using SMIC N+2:")
    print(f"  Transistor density gap: 310/90 = {310/90:.1f}x fewer transistors")
    print(f"  But inference ASIC removes ~30% non-essential GPU logic")
    print(f"  Effective density gap (after removal): {310/90*0.7:.1f}x")
    print(f"  Custom data format (UE8M0): ~2x compute efficiency")
    print(f"  Effective gap: {310/90*0.7/2:.1f}x — still significant but narrower")
    print(f"  → Architectural innovation is not optional; it's existential")


if __name__ == "__main__":
    process_node_analysis()