Author

Jeebak Mitra

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#artificial intelligence Preprint Oct 2026

Characterizing Parallelism Strategies in LLM Inference: Fundamental Compute-Communication Trade-offs

Large Language Model (LLM) inference has become the dominant workload in modern AI systems, requiring serving infrastructures to maximize throughput while meeting strict latency Service-Level Objectives (SLOs). Since state-of-the-art LLMs exceed the compute and memory capacity of a single GPU, inference is commonly dis...

Javad Mirzaei, Jeebak Mitra · 0 citations

#machine learning Preprint Jul 2026

Optimizing AI Inference Across the Deployment Stack

A unified analytical treatment of inference optimization across the deployment stack with a three-layer taxonomy covering model-level techniques such as quantization, pruning, and distillation; compiler transformations such as graph fusion, layout optimization, and kernel autotuning; and system policies such as dynamic...

Tejinder Singh, John Pflueger, Jeebak Mitra et al. · 1 citation