Visual flowchart/flow diagram would be displayed here
Technical Implementation: ```python
class ResourceCalculator:
def init(self):

Typical values for resource estimation

self.bytes_per_parameter = 4

32-bit floating point

self.training_memory_multiplier = 4

Gradients, optimizer states, etc.

def calculate_model_size(self, parameter_count):
    """Calculate model size in different units"""
    bytes_size = parameter_count * self.bytes_per_parameter

    return {
        'parameters': parameter_count,
        'model_size_bytes': bytes_size,
        'model_size_mb': bytes_size / (1024 * 1024),
        'model_size_gb': bytes_size / (1024 * 1024 * 1024)
    }

def estimate_training_requirements(self, parameter_count):
    """Estimate training resource requirements"""
    model_info = self.calculate_model_size(parameter_count)

Training typically requires 3-4x model size for gradients and optimizer

    training_memory_gb = model_info['model_size_gb'] * self.training_memory_multiplier

Rough estimate: 1B parameters ≈ 1 GPU-day for training

    estimated_gpu_days = parameter_count / 1_000_000_000

    return {
        'model_size_gb': model_info['model_size_gb'],
        'training_memory_gb': training_memory_gb,
        'estimated_gpu_days': estimated_gpu_days,
        'estimated_cost_range': f"${estimated_gpu_days * 1000:.0f} - ${estimated_gpu_days * 5000:.0f}"
    }

def compare_model_requirements(self):
    """Compare resource requirements for different model sizes"""
    model_sizes = [
        ('Small Model', 100_000_000),

100M

        ('Medium Model', 1_000_000_000),

1B

        ('Large Model', 10_000_000_000),

10B

        ('Very Large Model', 100_000_000_000),

100B

        ('Massive Model', 685_000_000_000)

685B

    ]

    print("Model Resource Requirements Comparison:")
    print("=" * 80)
    print(f"{'Model':<15} {'Size (GB)':<12} {'Training RAM':<15} {'GPU-Days':<12} {'Cost Est.'}")
    print("-" * 80)

    for model_name, param_count in model_sizes:
        requirements = self.estimate_training_requirements(param_count)

        print(f"{model_name:<15} "
              f"{requirements['model_size_gb']:<12.1f} "
              f"{requirements['training_memory_gb']:<15.1f} "
              f"{requirements['estimated_gpu_days']:<12.1f} "