How Many Gpus Are Needed To Train Gpt4-O

Generated with sparks and insights from 40 sources

Introduction

Number of GPUs: Training GPT-4 required approximately 25,000 Nvidia A100 GPUs.
Training Duration: The training process took between 90 to 100 days.
Energy Consumption: The servers used about 6.5 kW each, resulting in an estimated 50 GWh of energy usage during training.
Cost: The total cost of training GPT-4 was around $100 million, with cloud expenses alone amounting to approximately $60 million.
Parameters: GPT-4 has around 1.7 trillion parameters and was trained on 13 trillion tokens.

Duration: The training of GPT-4 took between 90 to 100 days.
GPU Utilization: Approximately 25,000 Nvidia A100 GPUs were used simultaneously during this period.
Training Efficiency: The GPUs were running at about 32% to 36% of their maximum theoretical utilization.

Total Energy: The training of GPT-4 consumed approximately 50 GWh of electricity.
Server Power Usage: Each server with Nvidia A100 GPUs used about 6.5 kW.
Comparison: This energy usage is about 0.02% of the electricity California generates in a year.

Total Cost: The cost of training GPT-4 was around $100 million.
Cloud Expenses: Cloud costs alone were approximately $60 million, assuming $1 per A100 GPU hour.
Comparison: Training GPT-3 cost around $4.6 million and took 34 days with 1,024 Nvidia V100 GPUs.

Parameter Count: GPT-4 has around 1.7 trillion parameters.
Token Count: The model was trained on approximately 13 trillion tokens.
Training Epochs: GPT-4 underwent 2 epochs for text-based data and 4 epochs for code-based data.

Carbon Emissions: Training GPT-4 emitted between 12,456 and 14,994 metric tons of CO2.
Energy Source: The environmental impact varies significantly based on the location of the data centers and their energy sources.
Comparison: Training GPT-4 in Northern Sweden would be equivalent to driving an average car around the globe 300 times.

Inference Cost: The inference costs for GPT-4 are approximately three times those of its predecessor, Davinchi.
Cluster Size: GPT-4 requires larger clusters for its operation, involving multiple clusters distributed across different data centers.
Parallelism Techniques: The inference process uses 8-way tensor parallelism and 16-way pipeline parallelism to manage computational demands.