Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions
Scheduling LLM Inference with Uncertainty-Aware Output Length Predictions explores An uncertainty-aware scheduling metric for LLM inference that significantly reduces latency and improves throughput by accounting for output length variability.. Commercial viability score: 4/10 in LLM Inference Scheduling.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisDuration Aware Scheduling for ASR Serving Under Workload DriftViability: 3.0
- Builds On ThisFlowPrefill: Decoupling Preemption from Prefill Scheduling Granularity to Mitigate Head-of-Line Blocking in LLM ServingViability: 2.0
- Builds On ThisSuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on SuperchipsViability: 3.0
- Higher ViabilityPredicting LLM Output Length via Entropy-Guided RepresentationsViability: 5.0
- Higher ViabilityEWSJF: An Adaptive Scheduler with Hybrid Partitioning for Mixed-Workload LLM Inference