Reliability Testing of Medical Model Performance under Distributed Deployment
Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Although modern frameworks improve serving efficiency through tensor parallelism, mixed precision, kernel fusion, and multi-device communication, they are generally assumed to preserve the behavior observed during centralized HuggingFace evaluation. This assumption creates an evaluation-deployment mismatch: a model may pass offline evaluation but produce a different output after the execution stack changes. To address this mismatch, we propose a testing framework and an improved, distributed-execution-sensitive medical-model benchmark that evaluat...
| Authors | Yifei Wang, Xiaohan Zhang, Youtao Ding et al. |
|---|---|
| Primary category | eess.IV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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