Case Studies / AI
AI · OptimizationThe same AI. 92% cheaper to run.
FP32 language models quantized to INT8 — GPU-class intelligence on standard CPUs.
92%
Cost reduction
6x
Faster inference
CPU-only
Deployment
The ClientAFS & PathMetrics
01
The Challenge
Full-precision LLMs were too heavy for real-time use.
✕$300/hr compute for unquantized models
✕Up to 3.4 minutes per query — unusable in real time
✕No path to edge or low-resource deployment
02
What We Built
A quantization pipeline converting FP32 LLMs into INT8 small language models.
✓Model size and hardware demands cut without meaningful accuracy loss
✓Inference moved from GPUs to standard CPUs
✓Edge deployment on 16GB RAM (vs 80GB VRAM previously)
03
The Stack
PythonFP32→INT8 quantization toolchainCPU inference
04
The Results
Numbers from production operation — not projections.
92%
Inference cost reduction ($300 → $24/hr)
6x
Faster inference (6.2s → 0.96s)
16GB
RAM instead of 80GB VRAM
MORE
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