{"total":20,"rows":[{"id":"2408.00741","title":"DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency","created":"2024-08-01","cited_by_count":112,"area":null},{"id":"2401.08092","title":"A Survey of Resource-efficient LLM and Multimodal Foundation Models","created":"2024-01-16","cited_by_count":86,"area":null},{"id":"2407.12391","title":"LLM Inference Serving: Survey of Recent Advances and Opportunities","created":"2024-07-17","cited_by_count":46,"area":null},{"id":"2407.00326","title":"Teola: Towards End-to-End Optimization of LLM-based Applications","created":"2024-06-29","cited_by_count":32,"area":null},{"id":"2404.14527","title":"M\\'elange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity","created":"2024-04-22","cited_by_count":31,"area":null},{"id":"2501.02600","title":"TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms","created":"2025-01-05","cited_by_count":29,"area":null},{"id":"2410.07265","title":"A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models","created":"2024-10-08","cited_by_count":23,"area":null},{"id":"2411.01438","title":"SkyServe: Serving AI Models across Regions and Clouds with Spot Instances","created":"2024-11-03","cited_by_count":20,"area":null},{"id":"2403.12900","title":"Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference","created":"2024-03-19","cited_by_count":13,"area":null},{"id":"2410.00531","title":"TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices","created":"2024-10-01","cited_by_count":11,"area":null},{"id":"2402.18789","title":"FlexLLM: Token-Level Co-Serving of LLM Inference and Finetuning with SLO Guarantees","created":"2024-02-29","cited_by_count":9,"area":null},{"id":"2410.01228","title":"ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving","created":"2024-10-02","cited_by_count":5,"area":null},{"id":"2405.08965","title":"MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming","created":"2024-05-14","cited_by_count":3,"area":null},{"id":"2509.26534","title":"Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2512.22492","title":"Role-Based Fault Tolerance System for LLM RL Post-Training","created":"2025-12-27","cited_by_count":2,"area":null},{"id":"2501.10187","title":"Good things come in small packages: Should we build AI clusters with Lite-GPUs?","created":"2025-01-17","cited_by_count":1,"area":null},{"id":"2510.13668","title":"STAR: Decode-Phase Rescheduling for LLM Inference","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2604.20420","title":"Scalable AI Inference: Performance Analysis and Optimization of AI Model Serving","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2607.17181","title":"Talaria: Session-Aware Serverless Serving of Hundred-Billion-Parameter LLMs","created":"2026-07-19","cited_by_count":0,"area":null},{"id":"2609.37902","title":"You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}