329 posts in total. Keep on posting.

2026

• 04-10 中

  SVD-LLM：面向大语言模型压缩的“截断感知”奇异值分解方法 — 深度阅读笔记

  1. 这篇论文为什么值得认真读 我先用一句话给结论： SVD-LLM 把 SVD 压缩里最关键的“截断决策”从经验做法，升级成和真实压缩损失直接对齐的数学机制；再通过顺序式低秩参数更新，把高压缩率下的质量崩塌拉回来。 为什么这是大事？因为现实里的 LLM 部署痛点非常具体： 权重太大，显存压力爆炸； 线上推理成本高； 压缩到中高比例时，很多方法会“看起来压了，实际不可用”。 SVD 路线一直很诱人：硬件友好、结构规整、并且可同时影响权重与推理内存。但过去的 SVD 方案在高压缩率下经常掉得太厉害。SVD-LLM 的意义就在于，它不是“调了一点超参”，而是把根因给修了。

• 04-10 EN

  SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression — Deep Technical Review

  1. Why this paper matters If I must explain this paper to a complete beginner in one sentence: SVD-LLM makes low-rank compression for large language models much more reliable by fixing two core problems in older SVD methods: wrong truncation guidance and no post-truncation recovery update. This matters because today’s LLM deployment pain is not abstract: model weights are huge, memory budgets are real, latency and hardware costs are painful, and many “easy compression” methods collapse at high compression ratios. SVD-based compression has always looked attractive because it is hardware-friendly and can reduce both parameter memory and runtime footprint, but prior SVD compression pipelines (for LLMs) often become unstable or degrade sharply when compression ratio gets aggressive. SVD-LLM’s contribution is to make SVD truncation mathematically aligned with loss and then recover quality through a sequential low-rank parameter update. In experiments, that combination is exactly where the big gap appears.

• 04-09 中

  DistServe：通过 Prefill/Decoding 解耦实现面向 Goodput 的大模型服务优化 — 深度阅读笔记

  1. 这篇论文为什么值得认真读 先给一句结论： DistServe 的价值在于：它把大模型服务里最痛的“延迟-成本矛盾”从调度层面硬扛，升级成“架构层面解耦”，然后再用算法做资源与并行策略的联合优化。 很多服务系统论文会给你“吞吐更高”的漂亮数字，但实际线上产品最在意的是： 用户是否足够快看到首个 token（TTFT）？ 后续生成是否足够流畅（TPOT）？ 在满足服务质量（SLO）的前提下，每块 GPU 的产出值不值？ DistServe 不是只追求 token/s，而是明确优化 goodput（满足 SLO 约束下的单位 GPU 可服务请求率）。这点非常“工程真相”。 论文报告的核心收益是： 相比主流方案，最高 7.4× 请求率提升； 或在同样请求率下，实现 12.6× 更严格 SLO； 且 >90% 请求满足延迟约束。 这是“性能数字 + 用户体验 + 成本效率”三者同时进步，而不是单指标提升。

• 04-09 EN

  DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving — Deep Technical Review

  1. Why This Paper Matters If I explain this paper in one sentence: DistServe improves LLM serving under latency SLOs by separating prefill and decoding onto different GPU pools, then jointly optimizing their resources and placement to maximize goodput per GPU. This sounds simple, but it addresses one of the deepest frustrations in real-world LLM systems engineering: product wants both fast first response and smooth generation, infrastructure wants high utilization and low cost, and existing colocated serving designs often force a painful compromise. The paper makes this problem concrete, quantifies why the compromise happens, proposes a practical architecture, and validates it with strong end-to-end gains: up to 7.4× higher request rate at target SLO attainment, or 12.6× tighter SLOs at fixed rate, while keeping latency constraints satisfied for >90% of requests. That combination (clear diagnosis + design + measurable gains + deployment details) is why this is a serious ML systems paper.

• 04-08 EN

  SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models — In-Depth Technical Review

  1. Why This Paper Matters If you only remember one sentence from this review, I want it to be this: SmoothQuant is important because it turns a seemingly annoying numerical issue—activation outliers—into a clean systems trick that real hardware can actually use. Large language models are expensive for two reasons: they store a huge amount of weights, and they repeatedly move those weights and activations through matrix multiplications. That means memory footprint, memory bandwidth, and integer-kernel friendliness are not side details. They are central engineering constraints.

• 04-08 中

  SmoothQuant：大型语言模型的精准高效训练后量化 — 深度阅读笔记

  SmoothQuant 通过把激活值中的离群难度迁移到权重中，让大模型能够更稳定地实现高效 W8A8 推理。

• 04-07 EN

  ORPO: Monolithic Preference Optimization without Reference Model — In-Depth Technical Review

  ORPO merges supervised fine-tuning and preference optimization into one objective, removing the need for a separate reference model while keeping strong alignment performance.

• 04-07 中

  ORPO：不用参考模型的一体化偏好优化 — 深度阅读笔记

  ORPO 将监督微调和偏好优化合并到一个目标中，在不依赖参考模型的情况下实现更简单的对齐训练。

• 04-04 EN

  Switch Transformers: Scaling to Trillion-Parameter Sparse Models — In-Depth Technical Review

  Switch Transformer routes each token to a single expert, making trillion-parameter sparse models practical with major training-speed gains and simpler MoE scaling.

• 04-04 中

  Switch Transformers：用简单高效的稀疏性扩展到万亿参数模型 — 深度阅读笔记

  Switch Transformer 将每个 token 只路由到一个专家，在相近计算量下实现稀疏万亿参数模型的高效训练。

• 04-03 EN

  AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration — In-Depth Technical Review

  AWQ uses activation-aware scaling to protect salient weights during low-bit quantization, enabling strong LLM accuracy with efficient deployment on edge and server hardware.

• 04-03 中

  AWQ：感知激活值的大模型权重量化压缩与加速 — 深度阅读笔记

  AWQ 利用激活感知缩放保护最关键的权重，在低比特量化下仍保持较强精度，并适合真实设备部署。