3 citations · 3 across the 3 of their papers we have counts for
Showing cs.PFShow all
2 papers · 1 filter
cs.PF2026
Simulate Before You Scale: High-Fidelity LLM Inference Planning Without GPUs
Dipanwita Guhathakurta, Mert Toslali, Srinivasan Parthasarathy +6
Deploying large language model (LLM) inference at scale requires jointly selecting hardware layout, parallelism strategy, scheduling policy, admission control, and request routing…
cs.PF2026
An Approximate Queueing Model of LLM Inference Serving for SLO-Driven Autoscaling
Vishakha Ramani, Asser N. Tantawi
Performance models of LLM servers support both latency evaluation and the design of controllers for autoscaling against service level objectives (SLOs) and for inference optimizati…