Raw Speed Race
Both systems race to complete the same task. GPU uses brute force at 700W.
Human Brain
86B neurons @ 20W
0%
Ops: 0
Time: 0.0s
Power: 20W
VS
0.0s
NVIDIA H100
16,896 cores @ 700W
0%
Ops: 0
Time: 0.0s
Power: 700W
💡 Understanding This Race
The GPU wins on raw speed by using 35× more power. But this isn't the whole story—
the brain achieves remarkable results while consuming only 20 watts (like two LED light bulbs).
Try the other modes to see where the brain excels.
Human Brain
Power Consumption
20W Limit
20W
✓ Operating at Full Capacity
Using 100% of available power budget
Task Progress
0%
NVIDIA H100
Power Consumption (Capped at 20W)
20W Limit (2.8% capacity)
20W (of 700W)
⚠ SEVERELY THROTTLED
Only 7 of 256 cores can run (2.8%)
Task Progress
0%
⚡ The Power Efficiency Story
When limited to the brain's 20W power budget, the GPU can only use 2.8% of its capacity.
The brain achieves ~50 trillion operations per watt, while the H100 manages ~5.7 trillion.
This 9× efficiency gap explains why biological intelligence evolved as it did—energy efficiency was survival.
Human Brain
0
training examples seen
Waiting to learn...
AI Neural Network
0
training examples seen
Waiting to learn...
Needs ~1,000,000 examples to learn this task
📚 Few-Shot vs Many-Shot Learning
Humans excel at "few-shot learning"—recognizing a new face from just one photo,
or learning a new word from a few sentences. AI typically needs thousands to millions of examples.
This is why AI training requires massive datasets and compute power. The brain's ability to generalize
from minimal data remains one of its most remarkable and poorly understood capabilities.
Human Brain
86 Billion
parallel processing units (neurons)
Power: 20 Watts
Cost: $0 (came free)
Cost: $0 (came free)
=
To match
parallelism
parallelism
GPU Datacenter Required
0 GPUs
Need: 8,600,000 H100 GPUs
(Each GPU = 16,896 cores)
8.6M
H100 GPUs
6.0 GW
Power (6 Nuclear Plants)
$258B
Hardware Cost
86 km²
Datacenter Space
🏗 The Scale Problem
To match the brain's 86 billion parallel units, you'd need 8.6 million H100 GPUs—
more than NVIDIA produces in years. The power requirement of 6 gigawatts equals
6 nuclear power plants running continuously. The brain fits in your skull and runs on a sandwich.
This is why neuromorphic computing and brain-inspired architectures are active research areas.