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HealthServe: A Public Health-Aware Inference Framework
- Truvace record
- TRV-2026-2603raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 9a60cdc9fac6…check it
Large language model (LLM) inference consumes substantial datacenter energy and produces both carbon emissions and criteria pollutants (PM2.5, SO2, NOx). These criteria pollutants impact public health depending on the meteorological conditions of emission regions and population exposure. We apply health impact assessment to LLM inference, a use case of growing concern due to its rapidly rising energy consumption, and characterize health impacts from datacenter energy consumption as quantifiable health cost metri…
The gain
In tests on a heterogeneous GPU cluster across U.S. datacenter locations, the HealthServe scheduler cut carbon footprint by over 50% and health costs by over 25% compared with state-of-the-art energy-aware LLM serving.
Sources
1 on file- ACM SIGMETRICS Performance Evaluation Reviewdoi:10.1145/3820171.3806868
Cite this record
v1Truvace Impact Record TRV-2026-2603, v1: “HealthServe: A Public Health-Aware Inference Framework.” Truvace, 2026-11-12. /record/TRV-2026-2603 (accessed on the day you copy it). sha256 9a60cdc9fac633f1…
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