Track F
Foundation
What quality per watt and per euro is possible when data never leaves the organization?
Scope: compute-spectrum efficiency study, private retrieval-augmented generation, phone-scale inference, offline self-learning document extraction with LoRA.
Why it matters
Many deployments cannot send data to a public cloud and cannot buy frontier-scale compute.
Directions
1. Efficiency across the hardware spectrum, from server to phone-scale inference.
Efficiency across the hardware spectrum, from server to phone-scale inference.
2. Private retrieval-augmented generation that stays inside the organization.
Private retrieval-augmented generation that stays inside the organization.
3. Offline self-learning document extraction with LoRA.
Offline self-learning document extraction with LoRA.
Output: reproducible benchmarks across the hardware spectrum, with raw logs. Measurements are not published yet.
Foundation question: quality versus cost
Empty chart. X axis is cost per answer. Y axis is quality. A dashed region is the target frontier. No measured points. Measurements pending.
Target frontier
Measurements pending
View as table
| Axis | Meaning |
|---|---|
| X | Cost per answer |
| Y | Quality |
| Region | Target frontier. Measurements pending |
Planned metrics
Values show Pending until measurements exist.
Quality per watt
Pending
Answer quality relative to energy used, on named hardware
Quality per euro
Pending
Answer quality relative to operating cost
Locality constraint adherence
Pending
Share of runs where data never left the designated boundary
Connects to
The lab designs and studies five research objects. They are planned artifacts on the agenda, not products with published measurements.
- Route policy
- Provenance graph
- Quality contract
- Private-compute stack
- Runtime monitors
Work with HOPN Lab
Enterprise buyers, academic collaborators, students and funders in Europe can write to us.