arXiv Machine Learning July 8, 2026 By Amitash Nanda, Javier Hernandez Nicolau, Madhusudan Gujral, Mahidhar Tatineni, Amitava Majumdar, Debashis Sahoo

Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators

Read the original on arXiv Machine Learning →

arXiv:2607. 05400v1 Announce Type: cross Abstract: Generative AI models, such as Large Language Models (LLMs) and diffusion models, have demonstrated impressive performance across a wide range of tasks.

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arXiv AI

Jun 17

Prefill/Decode-Aware Evaluation of LLM Inference on Emerging AI Accelerators

arXiv:2606. 17104v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in latency- and cost-sensitive settings, inference efficiency has become a central systems challenge.

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llms efficiency

arXiv Machine Learning

Jul 14

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llms efficiency

arXiv AI

Aug 10

Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors

arXiv:2608. 06723v1 Announce Type: cross Abstract: The rapid scaling of Large Language Models (LLMs) has significantly increased computational cost, energy consumption, and inference latency, making accurate estimation essential for sustainable artificial intelligence deployment and hardware-aware design.

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llms efficiency

arXiv Computer Vision

2d ago

Hardware-Algorithm Co-Optimization of Early-Exit Neural Networks for Multi-Core Edge Accelerators

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arXiv Machine Learning

2d ago

QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs

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llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 11

Optimizing AI Inference Across the Deployment Stack

The paper argues that AI deployment performance depends on interactions among compression, compiler transformations, and serving policies rather than just model architecture. It introduces a three‑layer taxonomy—model‑level techniques, compiler transformations, and system policies—and frames deployment as a constrained multi‑objective optimization problem over accuracy, latency, throughput, memory footprint, and energy. The authors propose an evidence protocol for comparable benchmarking and synthesize data from edge and data‑center platforms to show that cross‑layer interactions drive deployment outcomes, concluding with a constraint‑aware selection procedure and open research problems.

By Tejinder Singh, John Pflueger, Jeebak Mitra, Robert Lincourt, Mitchell Markow, Bhavesh A. Patel

llms efficiency benchmarks

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