Model Quantization
Shrink model weights to fit on phones and save on GPU costs.
Efficiency Stats
Memory Footprint (VRAM)
16 GB
Inference Throughput
1.0x
Current: FP32 Precision
* Lower precision = Lower cost / Faster speed
Neural Weight Matrix
0.603
-0.44
0.293
0.142
-0.38
0.375
0.767
0.710
0.323
-0.08
-0.07
-0.75
-0.64
-0.73
-0.86
0.275
0.569
0.421
0.246
0.856
0.385
0.287
0.920
-0.40
0.986
-0.03
-0.59
0.167
0.527
-0.79
-0.35
-0.82
0.105
0.373
0.752
0.841
0.330
0.031
0.403
0.672
0.459
-0.83
0.160
-0.33
0.879
0.079
-0.78
-0.13
The JPEG for Neural Nets
Most model weights are high-precision numbers (32-bit floating points). Quantization compresses these numbers into 8-bit or 4-bit integers. Surprisingly, models retain 95%+ of their intelligence even after this massive data reduction.
Why Founders Care
Quantization is the difference between needing an $30k A100 GPU cluster or running your AI on a single consumer-grade desktop or MacBook. It slashes hosting costs and makes "Local AI" features possible.