Energy-aware LLM serving requires comparing configurations under realistic request shapes, yet exhaustive target-GPU profiling is costly and a cheap predictor can be dangerously confident outside its measured scope. We present TokenPowerSandbox, an evidence-gated workflow that combines an interpretable CPU-resident projector, short target-GPU probes, full-workload verification, and tamper-evident freeze-before-measurement provenance. On one NVIDIA H100 80GB serving Qwen2.5-7B-Instruct with vLLM, three anchor repeats and six development workloads calibrate workload transfer. The same frozen model is evaluated on a blind holdout and a separately predeclared no-refit confirmation totaling 51 post-freeze runs. Energy MAPE is 6.23% and 7.35%, with Spearman rank correlations of 0.976 and 0.933. However, a predeclared TTFT gate passes at concurrency four (9.27% MAPE) and triggers abstention below four (64.80%), showing why energy accuracy cannot certify latency.
With a trace-driven, event-atomic simulator over three MoE models, a large offline-optimal gap substantially overstates the gains recovered by representative lightweight causal mechanisms.
This work presents KVDiagnosis, a diagnostic dataset and benchmark with three contributions, a 25-method taxonomy that groups methods into five mechanism families and links them to eight verified implementations and their valid diagnostic measurements, and a common record format.
Hydra is presented, a common-schema, phase-aware workload characterization framework for LLM inference on edge SoCs that enables reproducible, phase-aware characterization of edge LLM inference.
Amir Taherin, Sana Taghipour Anvari, Charles Amante et al.· 2 citations
KV-cache offload is widely used to stretch GPU memory for LLM serving, but its storage behavior has not been characterized at the block-device level. In this paper, we study LMCache through realworld multi-session workloads that span same/different context $\times$ same/different prompt, using over 100 stateless reques...
Ying He, Dingsen Shi, Yanbo Dai et al.· 2026 International Conferenc...· 0 citations
OasisKV is presented, a memory-centric LLM inference system design that alleviates HBM capacity pressure by decoupling full KV-cache storage from HBM during LLM decoding and observes that future important tokens can be predicted accurately in advance using lookahead tokens drafted by speculative decoding (SD).
MaverIQ is an intent-based LLM inference serving system that automatically maps user intents to deployment configurations while minimizing operational cost for the provider and reduces profiling overheads by 7-15× compared to state-of-the-art baselines.
Dimitrios Liakopoulos, Prasoon Sinha, Tianrui Hu et al.· ACM SIGOPS Operating Systems...· 0 citations
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