# Panoramic Research on Large Language Model Systems: A Systematic Paper Resource Library from Training to Deployment

> A carefully curated list of academic papers, tutorials, and project resources related to large language model systems, covering key areas such as pre-training, post-training, inference services, multimodal systems, and Agent systems. It is a must-read guide for LLM system researchers.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-21T04:43:21.000Z
- 最近活动: 2026-06-21T04:48:34.182Z
- 热度: 145.9
- 关键词: 大语言模型系统, 分布式训练, 模型并行, 推理优化, LLM Serving, 多模态系统, Agent系统, 机器学习系统, GPU集群优化, MoE训练
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-amberljc-llmsys-paperlist
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-amberljc-llmsys-paperlist
- Markdown 来源: floors_fallback

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## [Introduction] Overview of the Panoramic Resource Library for LLM System Research

The GitHub repository LLMSys-PaperList, maintained by AmberLJC, continuously updates academic papers, tutorials, and project resources related to large language model systems. It covers key areas such as pre-training, post-training, inference services, multimodal systems, and Agent systems, serving as a must-read guide for LLM system researchers and systematically outlining the evolution of this field from 2019 to the present.

## Background: The Importance of LLM System Research and the Significance of the Resource Library

With the explosive development of large language models like ChatGPT and Claude, the AI field faces stringent requirements for underlying system architectures. From optimizing distributed training parallelism strategies to balancing inference service latency and throughput, and improving multimodal data processing efficiency, system-level innovations are needed. As an academic literature map, LLMSys-PaperList provides a systematic overview for researchers in this field.

## Resource Library Overview: Core Areas Covering the Entire Lifecycle of LLM Systems

The resource library divides core areas by technology stack and application scenarios:
1. Pre-training systems: e.g., Megatron-LM model parallelism scheme, ByteDance's MegaScale 10k-GPU training practice;
2. Post-training and RLHF systems: efficient fine-tuning strategies, reward model training, and distributed implementation of PPO;
3. Inference service systems: memory management, KV cache optimization, dynamic batching, and edge device operation;
4. Agent systems: tool call optimization, multi-turn dialogue state management, and secure interaction with external systems;
5. Multimodal systems: data loading related to GPT-4V/Gemini, feature extraction, and challenges in cross-modal attention computation.

## Key Technology Evolution: Breakthroughs and Frontiers of LLM Systems from 2024 to 2026

2024: ByteDance's MegaScale 10k-GPU training, DeepSeek-V3 MoE training optimization, Alibaba's HPN data center network architecture;
2025: ByteScale training of 2048K context models with 12000+ GPUs, TileLink programming primitives, Flex Attention optimization;
2026: AXLearn modular training for heterogeneous infrastructure, Arena dynamic scheduling and adaptive parallelism co-design.

## Practical Value: How Different Groups Can Benefit from the Resource Library

System researchers: Obtain a complete literature map to quickly locate classic and top conference frontier results;
Engineering practitioners: Learn industry practice experience (fault recovery, load balancing, network optimization, etc.);
Learners: Get a systematic learning path through resources like MLSys courses.

## Conclusion: The Era Opportunities of LLM System Research and the Sustained Significance of the Resource Library

LLM system research is in a period of rapid development, with new hardware, algorithms, and system innovations emerging one after another. This resource library not only organizes existing achievements but also serves as a continuously updated knowledge hub, providing a reliable guide for researchers, engineers, and learners to explore the frontiers of LLM systems.