Visual flowchart/flow diagram would be displayed here
Technical Implementation: ```python
class DeploymentConsiderations:
def init(self):
self.deployment_targets = {
'mobile': {'memory_limit_gb': 2, 'compute_limited': True},
'edge_device': {'memory_limit_gb': 8, 'compute_limited': True},
'cloud_cpu': {'memory_limit_gb': 64, 'compute_limited': False},
'cloud_gpu': {'memory_limit_gb': 24, 'compute_limited': False},
'server_farm': {'memory_limit_gb': 1000, 'compute_limited': False}
}

def assess_deployment_options(self, parameter_count):
    """Determine suitable deployment targets for a model"""

    model_size_gb = (parameter_count * 4) / (1024**3)

4 bytes per param

    suitable_targets = []

    for target, limits in self.deployment_targets.items():
        if model_size_gb <= limits['memory_limit_gb']:

Estimate inference time (very rough)

            if limits['compute_limited']:
                inference_time_ms = parameter_count / 1_000_000

1ms per million params

            else:
                inference_time_ms = parameter_count / 10_000_000

0.1ms per million params

            suitable_targets.append({
                'target': target,
                'memory_usage_percent': (model_size_gb / limits['memory_limit_gb']) * 100,
                'estimated_inference_ms': inference_time_ms
            })

    return {
        'model_size_gb': model_size_gb,
        'suitable_targets': suitable_targets
    }

def create_deployment_guide(self):
    """Create deployment recommendations for different model sizes"""

    model_categories = [
        ('Tiny Model', 1_000_000, 'Mobile apps, IoT devices'),
        ('Small Model', 10_000_000, 'Mobile apps, edge computing'),
        ('Medium Model', 100_000_000, 'Desktop apps, edge servers'),
        ('Large Model', 1_000_000_000, 'Cloud services, dedicated servers'),
        ('Very Large Model', 10_000_000_000, 'High-end cloud, GPU clusters'),
        ('Massive Model', 100_000_000_000, 'Specialized infrastructure only')
    ]

    print("Model Deployment Guide:")
    print("=" * 80)

    for name, param_count, use_cases in model_categories:
        analysis = self.assess_deployment_options(param_count)

        print(f"\n{name} ({param_count/1_000_000:.0f}M parameters):")
        print(f"  Model size: {analysis['model_size_gb']:.2f} GB")
        print(f"  Typical use cases: {use_cases}")
        print(f&qu