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 5
assive Model', 100_000_000_000)
# 100B
]
print("Training Feasibility Analysis:")
print("=" * 80)
for model_name, param_count in model_sizes:
analysis = self.assess_training_feasibility(param_count)
print(f"\n{model_name} ({param_count/1_000_000:.0f}M parameters):")
print(f" Model size: {analysis['model_size_gb']:.2f} GB")
print(f" Training memory needed: {analysis['training_memory_required_gb']:.2f} GB")
if analysis['feasible_hardware']:
cheapest = min(analysis['feasible_hardware'],
key=lambda x: x['daily_cost'])
print(f" Cheapest option: {cheapest['hardware']} (${cheapest['daily_cost']}/day)")
else:
print(" No single GPU can train this model - need distributed training")
training_analysis = TrainingImplications()
training_analysis.compare_training_scenarios()