arXiv Machine Learning July 30, 2026 By Ming-Yen Lee, Hanchen Yang, Faaiq Waqar, Harsono Simka, Tushar Krishna, Muhammed Ahosan Ul Karim, Shimeng Yu

LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving

Read the original on arXiv Machine Learning →

arXiv:2607. 26491v1 Announce Type: cross Abstract: The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and thermal constraints and rising electricity costs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

llms

arXiv AI

Aug 10

Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference

arXiv:2604. 26968v2 Announce Type: replace-cross Abstract: Key-value (KV) cache memory management is the primary bottleneck limiting throughput and cost-efficiency in large-scale GPU inference serving.

By Sanjeev Rao Ganjihal

agents efficiency

arXiv Machine Learning

Jun 5

Tangram: Unlocking Non-Uniform KV Cache for Efficient Multi-turn LLM Serving

arXiv:2606. 06302v1 Announce Type: new Abstract: Multi-turn Large Language Model (LLM) serving is critical for consistent user experiences, yet the linear growth of the Key-Value (KV) cache imposes significant pressure on GPU memory and bandwidth.

By Hyungmin Kim, Minsoo Kim, Hongseok Kim, Jungwook Choi

llms efficiency

arXiv AI

Jul 1

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

arXiv:2603. 16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.

By Ruijia Yang, Zeyi Wen

llms fine-tuning efficiency

Hugging Face Trending Papers

Jun 4

Tangram: Unlocking Non-Uniform KV Cache for Efficient Multi-turn LLM Serving

Multi-turn Large Language Model (LLM) serving is critical for consistent user experiences, yet the linear growth of the Key-Value (KV) cache imposes significant pressure on GPU memory and bandwidth. Non-uniform KV compression effectively preserves more information by considering the individual importance of each KV cache.

llms efficiency

arXiv AI

Jun 24

CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation

arXiv:2606. 24506v1 Announce Type: cross Abstract: Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold.

By Zhuoren Ye, Tianyu Wo, Dinghao Xue, Mingming Zhang, Yuchen Teng, Chunming Hu, Renyu Yang

llms benchmarks

arXiv AI

Jun 30

KernelSight-LM: A Kernel-Level LLM Inference Simulator

arXiv:2606. 28565v1 Announce Type: cross Abstract: As large language models (LLMs) move into production serving, practitioners must rapidly evaluate inference performance across diverse hardware, models, and serving parameters to meet cost and latency targets.

By Xiteng Yao, Taeho Kim, Hengzhi Pei, Xinle Liu, Kyle Ulrich, Leonard Lausen, Ashish Khetan, Xiang Song, George Karypis, Martin Herbordt

llms efficiency benchmarks

Related stories

Predictive Multi-Tier Memory Management for KV Cache in Large-Scale GPU Inference

Tangram: Unlocking Non-Uniform KV Cache for Efficient Multi-turn LLM Serving

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

Tangram: Unlocking Non-Uniform KV Cache for Efficient Multi-turn LLM Serving

CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation

KernelSight-LM: A Kernel-Level LLM Inference Simulator