LENS predicts NPU LLM inference latency with 2.15% mean error by profiling each bucket with two E2E measurements and composing results to capture bucketing non-linearity.
Forecasting llm inference performance via hardware-agnostic analytical modeling,
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WattGPU ML models predict LLM inference power and latency on unseen GPUs with median errors of 3.4-13.5% using public data and show better performance than baselines.
Recover-LoRA with synthetic-data distillation recovers 80-95% accuracy on most benchmarks after selective 2-bit quantization of MLP gate/up layers while delivering 7.5-23.3% throughput improvement.
citing papers explorer
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Latency Prediction for LLM Inference on NPU Systems
LENS predicts NPU LLM inference latency with 2.15% mean error by profiling each bucket with two E2E measurements and composing results to capture bucketing non-linearity.
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WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs
WattGPU ML models predict LLM inference power and latency on unseen GPUs with median errors of 3.4-13.5% using public data and show better performance than baselines.