Inference & quantization optimization
INT4/FP8 quantization, semantic routing and right-sized GPU fleets.
AWQ / GPTQ · prompt compression · up to 70% cost reduction
Most AI bills are architecture mistakes. We quantize models within verified accuracy tolerances, route easy requests to small models, and compress prompts, cutting compute spend by up to 70%.
What you get
- Quantized models with verified accuracy deltas
- Semantic routing gateway and cache
- Prompt compression pipeline
- Before/after cost and latency report
How it works
- 01 · Profile current spend and latency distribution
- 02 · Quantize and verify accuracy tolerance
- 03 · Deploy routing, caching and compression
- 04 · Measure the delta and hand over dashboards
Good fit if
- API or GPU bills grow faster than usage
- Latency budgets are slipping
- You suspect your models are oversized
Deploy custom AI on your terms.
Tell us about your target silicon, data residency constraints, or latency requirements. Rochon engineers will provide a clear evaluation of what is viable, with power and latency models and deployment blueprints.