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Sai Kapil Kumar

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#artificial intelligence Preprint Sep 2026

The Inference Engineering Pareto Atlas: Which Optimizations Dominate the Cost, Quality, and Latency Frontier?

A cost, quality, and latency Pareto atlas is built to identify the best configurations for different deployment constraints and shows why speed alone is insufficient in vLLM inference, showing why speed alone is insufficient.

Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Where Should the KV Cache Live? Placement Policies Across GPU, CPU, and SSD for Long-Lived Sessions

This work compares recency, reuse frequency, predicted reuse, and an EWMA predictor with prefetch lookahead across chat, agent, and document question answering workloads, and concludes that reuse frequency performs best for agents and document question answering and prefetching does not justify its bandwidth cost.

Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving

A router is studied that estimates the additional completion time on each instance using exact prompt length, predicted output length, post admission KV cache pressure, and SLO class to match the goodput of round robin using six GPUs instead of seven.

Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri et al. · 1 citation

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