Explains model parameters, tokens, and scaling laws in plain language. Learn how size, data quality, and training choices affect cost and accuracy—and how to pick the right model for a task.
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🔧 What Are AI Model Parameters? — Conceptual Process — Part 4
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### Conceptual Process
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Technical Implementation: ```python
class TrainingImplications:
def __init__(self):
self.hardware_capabilities = {
'consumer_gpu': {'memory_gb': 8, 'daily_cost': 0},
'professional_gpu': {'memory_gb': 24, 'daily_cost': 50},
'cloud_gpu_v100': {'memory_gb': 32, 'daily_cost': 75},
'cloud_gpu_a100': {'memory_gb': 80, 'daily_cost': 150},
'tpu_pod': {'memory_gb': 1000, 'daily_cost': 1000}
}
def assess_training_feasibility(self, parameter_count):
"""Assess what hardware is needed for training a model"""
# Rough estimates for training memory requirements
model_memory_gb = (parameter_count * 4) / (1024**3)
# 4 bytes per param
training_memory_gb = model_memory_gb * 4
# 4x for gradients, optimizer states
feasible_hardware = []
for hardware, specs in self.hardware_capabilities.items():
if specs['memory_gb'] >= training_memory_gb:
feasible_hardware.append({
'hardware': hardware,
'memory_gb': specs['memory_gb'],
'daily_cost': specs['daily_cost'],
'memory_utilization': training_memory_gb / specs['memory_gb']
})
return {
'parameter_count': parameter_count,
'model_size_gb': model_memory_gb,
'training_memory_required_gb': training_memory_gb,
'feasible_hardware': feasible_hardware
}
def compare_training_scenarios(self):
"""Compare training feasibility for different model sizes"""
model_sizes = [
('Small Model', 10_000_000),
# 10M
('Medium Model', 100_000_000),
# 100M
('Large Model', 1_000_000_000),
# 1B
('Very Large Model', 10_000_000_000),
# 10B
('M