Execution
Task Count
64
Parallel Cores
32
Clock Speed (Hz)
150
Simulation Speed
1×
Sequential (CPU-like)
1 processing unit @ 150 Hz
0%
Complete
CPU
Idle
Task Progress
Elapsed Time
0.00s
Parallel (GPU-like)
32 processing units @ 150 Hz
0%
Complete
Task Progress
Elapsed Time
0.00s
Sequential Time
—
64 tasks × 20 cycles each
Parallel Time
—
32 cores working together
Speedup Factor
—
Parallel / Sequential
Efficiency
—
Speedup / Core Count
Real-World Scaling Comparison
MLP Forward
3.3s
0.1s
Attention (n=8)
16s
0.3s
Transformer Block
42s
0.6s
GPT-2 Token
1.3 yr
~50ms
💡 The Core Insight
Neural network operations are embarrassingly parallel. Matrix multiplications,
attention computations, and activation functions can all be computed simultaneously across thousands of units.
At 150 Hz with 1 core, a GPT-2 token takes ~1.3 years.
At 1.5 GHz with 10,000 cores (modern GPU), that same token takes ~50 milliseconds.
The architecture is identical. The algorithms are identical. Parallelism is the entire difference. This is why AI progress tracked GPU advancement, and why NVIDIA became the most valuable company during the AI boom.
At 150 Hz with 1 core, a GPT-2 token takes ~1.3 years.
At 1.5 GHz with 10,000 cores (modern GPU), that same token takes ~50 milliseconds.
The architecture is identical. The algorithms are identical. Parallelism is the entire difference. This is why AI progress tracked GPU advancement, and why NVIDIA became the most valuable company during the AI boom.