docs: make Chinese README the default

2026-07-13 10:14:34 +00:00
parent 0e7572fce8
commit fd1433d717
1 changed files with 58 additions and 52 deletions
@@ -1,18 +1,24 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/LMCache/LMCache) · [上游 README](https://github.com/LMCache/LMCache/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 <div align="center">
  <p align="center">
    <img src="asset/logo.png" alt="lmcache logo" width="45%">
  </p>
  <h3 align="center">
-    A KV Cache Management Layer for Scalable LLM Inference
+    面向可扩展 LLM 推理的 KV Cache 管理层
  </h3>
    <hr width="78%">

  <h3 align="center">
-    <a href="https://blog.lmcache.ai/">Blog</a> |
-    <a href="https://docs.lmcache.ai/">Documentation</a> |
-    <a href="https://join.slack.com/t/lmcacheworkspace/shared_invite/zt-3zxjao8h0-lRfBfnLqbALOtLsWn2ITxA">Join Slack</a> |
-    <a href="https://docs.lmcache.ai/community/meetings.html">Community Meeting</a> |
-    <a href="https://github.com/LMCache/LMCache/issues/2923">Roadmap</a>
+    <a href="https://blog.lmcache.ai/">博客</a> |
+    <a href="https://docs.lmcache.ai/">文档</a> |
+    <a href="https://join.slack.com/t/lmcacheworkspace/shared_invite/zt-3zxjao8h0-lRfBfnLqbALOtLsWn2ITxA">加入 Slack</a> |
+    <a href="https://docs.lmcache.ai/community/meetings.html">社区会议</a> |
+    <a href="https://github.com/LMCache/LMCache/issues/2923">路线图</a>
  </h3>

  [![GitHub Repo stars](https://img.shields.io/github/stars/LMCache/LMCache?style=flat&logo=github)](https://github.com/LMCache/LMCache/stargazers)
@@ -21,35 +27,35 @@
  [![GitHub commit activity](https://img.shields.io/github/commit-activity/w/LMCache/LMCache)](https://github.com/LMCache/LMCache/graphs/commit-activity)
  [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/LMCache/LMCache/)

-  ⭐ **If LMCache helps you serve LLMs faster and cheaper, [give us a star](https://github.com/LMCache/LMCache) — it helps more teams discover the project.**
+  ⭐ **如果 LMCache 能帮助你更快、更便宜地部署 LLM 服务，请[为我们点个星](https://github.com/LMCache/LMCache) — 这能帮助更多团队发现该项目。**

 </div>

-## Updates
- [2026/05] 🔥 Agentic workload benchmark on AMD MI300X ([blog](https://blog.lmcache.ai/en/2026/05/12/benchmarking-lmcache-for-multi-turn-agentic-workloads-on-amd-mi300x/)).
- [2026/04] 🔥 LMCache's new multiprocess (MP) architecture release ([blog](https://blog.lmcache.ai/en/2026/04/03/lmcaches-new-architecture-boosts-moe-inference-performance-by-10x/)).
- [2026/03] LMCache at GTC 2026 ([post](https://www.linkedin.com/posts/lmcache-lab_llm-opensource-nvidiagtc-activity-7442721875664826369-pMAu?utm_source=share&utm_medium=member_desktop&rcm=ACoAADkIIvQBTyG53kXXX70OZdE5rhpllYQqmIA)).
- [2026/01] LMCache multi-node P2P CPU memory sharing, from experimental feature to production ([blog](https://blog.lmcache.ai/en/2026/01/21/p2p-1/)).
+## 更新
+- [2026/05] 🔥 基于 AMD MI300X 的 Agentic 工作负载基准测试（[博客](https://blog.lmcache.ai/en/2026/05/12/benchmarking-lmcache-for-multi-turn-agentic-workloads-on-amd-mi300x/)).
+- [2026/04] 🔥 LMCache 全新多进程（MP）架构发布（[博客](https://blog.lmcache.ai/en/2026/04/03/lmcaches-new-architecture-boosts-moe-inference-performance-by-10x/)).
+- [2026/03] LMCache 亮相 GTC 2026（[文章](https://www.linkedin.com/posts/lmcache-lab_llm-opensource-nvidiagtc-activity-7442721875664826369-pMAu?utm_source=share&utm_medium=member_desktop&rcm=ACoAADkIIvQBTyG53kXXX70OZdE5rhpllYQqmIA)).
+- [2026/01] LMCache 多节点 P2P CPU 内存共享，从实验特性走向生产（[博客](https://blog.lmcache.ai/en/2026/01/21/p2p-1/)).

 <details>
-<summary>More</summary>
+<summary>更多</summary>

- [2025/11] LMCache x CoreWeave accelerate efficient LLM inference for Cohere ([blog](https://blog.lmcache.ai/en/2025/10/29/breaking-the-memory-barrier-how-lmcache-and-coreweave-power-efficient-llm-inference-for-cohere/)).
- [2025/10] LMCache joins the PyTorch Foundation and Tensormesh unveiled ([blog](https://blog.lmcache.ai/en/2025/10/31/tensormesh-unveiled-and-lmcache-joins-the-pytorch-foundation/), [PyTorch](https://pytorch.org/blog/lmcache-joins-pytorch-ecosystem/)).
- [2025/09] NVIDIA Dynamo integrates LMCache, accelerating LLM inference ([blog](https://blog.lmcache.ai/en/2025/09/18/nvidia-dynamo-integrates-lmcache-accelerating-llm-inference/)).
- [2025/08] 🎉 LMCache hits 5,000+ GitHub stars ([blog](https://blog.lmcache.ai/en/2025/08/28/%f0%9f%8e%89-lmcache-hits-5000-github-stars-thank-you-community/)).
- [2025/08] LMCache supports gpt-oss (20B/120B) on day 1 ([blog](https://blog.lmcache.ai/en/2025/08/05/lmcache-supports-gpt-oss-20b-120b-on-day-1/)).
- [2025/07] Get faster LLM inference and cheaper responses with LMCache and Redis ([Redis blog](https://redis.io/blog/get-faster-llm-inference-and-cheaper-responses-with-lmcache-and-redis/)).
- [2025/07] LMCache extends its turbo-boost to multimodal models in vLLM V1 ([blog](https://blog.lmcache.ai/en/2025/07/03/lmcache-extends-its-turbo-boost-to-multimodal-models-in-vllm-v1/)).
- [2025/06] LLM Production Stack goes cross-hardware: AMD, Arm and Ascend ([blog](https://blog.lmcache.ai/en/2025/06/20/llm-production-stack-goes-cross-hardware-ascend-arm-and-amd-support-incoming/)).
+- [2025/11] LMCache x CoreWeave 为 Cohere 加速高效 LLM 推理（[博客](https://blog.lmcache.ai/en/2025/10/29/breaking-the-memory-barrier-how-lmcache-and-coreweave-power-efficient-llm-inference-for-cohere/)).
+- [2025/10] LMCache 加入 PyTorch Foundation，Tensormesh 正式发布（[博客](https://blog.lmcache.ai/en/2025/10/31/tensormesh-unveiled-and-lmcache-joins-the-pytorch-foundation/), [PyTorch](https://pytorch.org/blog/lmcache-joins-pytorch-ecosystem/)).
+- [2025/09] NVIDIA Dynamo 集成 LMCache，加速 LLM 推理（[博客](https://blog.lmcache.ai/en/2025/09/18/nvidia-dynamo-integrates-lmcache-accelerating-llm-inference/)).
+- [2025/08] 🎉 LMCache GitHub Star 数突破 5,000+（[博客](https://blog.lmcache.ai/en/2025/08/28/%f0%9f%8e%89-lmcache-hits-5000-github-stars-thank-you-community/)).
+- [2025/08] LMCache 首日即支持 gpt-oss（20B/120B）（[博客](https://blog.lmcache.ai/en/2025/08/05/lmcache-supports-gpt-oss-20b-120b-on-day-1/)).
+- [2025/07] 借助 LMCache 与 Redis 实现更快的 LLM 推理与更低成本的响应（[Redis 博客](https://redis.io/blog/get-faster-llm-inference-and-cheaper-responses-with-lmcache-and-redis/)).
+- [2025/07] LMCache 在 vLLM V1 中将加速能力扩展至多模态模型（[博客](https://blog.lmcache.ai/en/2025/07/03/lmcache-extends-its-turbo-boost-to-multimodal-models-in-vllm-v1/)).
+- [2025/06] LLM Production Stack 实现跨硬件支持：AMD、Arm 与 Ascend（[博客](https://blog.lmcache.ai/en/2025/06/20/llm-production-stack-goes-cross-hardware-ascend-arm-and-amd-support-incoming/)).

 </details>

-## About
+## 关于

-LMCache is a **KV cache management layer** for LLM inference. It turns KV cache from a temporary state into reusable *AI-native knowledge* that can be *stored* persistently, *reused* across multiple serving engines, *monitored* with an observability stack, and *transformed* for better generation quality. As a result, LMCache **reduces TTFT** (time-to-first-token) and **improves throughput**, especially for long-context agentic, multi-turn conversation, and knowledge-augmented workloads (e.g., RAG).
+LMCache 是面向 LLM 推理的 **KV cache 管理层**。它将 KV cache 从临时状态转化为可复用的 *AI 原生知识（AI-native knowledge）*，可被持久 *存储*、在多个 serving engine 间 *复用*、通过可观测性技术栈进行 *监控*，并 *转换* 以提升生成质量。因此，LMCache 能够 **降低 TTFT**（time-to-first-token，首 token 时间）并 **提升吞吐量**，尤其适用于长上下文 agentic、多轮对话以及知识增强类工作负载（例如 RAG）。

-LMCache is **vendor-neutral**. It can be used as a KV cache layer for a range of mainstream open-source serving engines, inference frameworks, hardware vendors, storage systems, and infrastructure providers. The vendor neutrality allows users to freely switch between serving engines and storage vendors, while reusing the stored KV caches.
+LMCache 具备 **厂商中立（vendor-neutral）** 特性。它可作为 KV cache 层，用于多种主流开源 serving engine、推理框架、硬件厂商、存储系统与基础设施提供商。厂商中立性使用户能够在不同 serving engine 与存储供应商之间自由切换，同时复用已存储的 KV cache。

 <p align="center">
 <picture>
@@ -59,62 +65,62 @@ LMCache is **vendor-neutral**. It can be used as a KV cache layer for a range of
 </picture>
 </p>

-### Key features
+### 核心特性

- **Engine-independent deployment**: LMCache, as a standalone daemon process, manages KV cache independently from the inference engine process, so that KV cache will not be lost even if the inference engine crashes (i.e., no fate-sharing with engines).
+- **与推理引擎解耦的部署**：LMCache 作为独立守护进程，与推理引擎进程分离管理 KV cache，因此即使推理引擎崩溃，KV cache 也不会丢失（即不与引擎命运共享）。

- **Persistent, tiered KV cache offloading and reuse**: Move KV caches out of GPU memory into a tiered storage hierarchy spanning CPU memory, local storage, and remote backends, enabling reuse across requests, sessions, and engine instances to reduce repeated prefill computation and improve TTFT.
+- **持久化、分层的 KV cache 卸载与复用**：将 KV cache 从 GPU 内存迁移至跨越 CPU 内存、本地存储与远程后端的分层存储体系，实现跨请求、会话与引擎实例的复用，减少重复的 prefill 计算并改善 TTFT。

- **Production-level KV cache observability**: LMCache provides a rich set of KV cache observability metrics, including typical Kubernetes metrics (health monitoring, performance diagnostics), KV-cache-specific metrics (request-level and token-level prefix cache hits, lifecycle, request-level KV cache performance), management metrics (user-specific usage), and more.
+- **生产级 KV cache 可观测性**：LMCache 提供丰富的 KV cache 可观测性指标，包括典型 Kubernetes 指标（健康监控、性能诊断）、KV cache 专属指标（请求级与 token 级前缀缓存命中、生命周期、请求级 KV cache 性能）、管理指标（用户级用量）等。

- **Pluggable storage and transport backends**: Easily integrate remote storage and KV transfer backends through a unified interface, enabling KV cache offloading and sharing across storage providers. Through this interface, LMCache supports storage backends including CPU RAM, local disk (SSD), Redis/Valkey, Mooncake, InfiniStore, S3-compatible object storage, NIXL, and GDS.
+- **可插拔的存储与传输后端**：通过统一接口轻松集成远程存储与 KV 传输后端，实现跨存储提供商的 KV cache 卸载与共享。借助该接口，LMCache 支持 CPU RAM、本地磁盘（SSD）、Redis/Valkey、Mooncake、InfiniStore、S3 兼容对象存储、NIXL 与 GDS 等存储后端。

- **Non-prefix KV reuse**: Extend KV reuse beyond prefix caching by reusing cached KV blocks at any position in the prompt. This leverages CacheBlend to selectively recompute tokens for quality recovery.
+- **非前缀 KV 复用**：借助 CacheBlend 在提示词任意位置复用已缓存的 KV 块，将 KV 复用扩展至前缀缓存之外，并有选择地重算 token 以恢复质量。

- **PD disaggregation and KV transfer**: Support KV cache transfer from prefill workers to decode workers over NVLink, RDMA, or TCP through transport layers such as NIXL.
+- **PD 解耦与 KV 传输**：通过 NIXL 等传输层，支持经 NVLink、RDMA 或 TCP 将 KV cache 从 prefill worker 传输至 decode worker。

- **Pluggable KV transformation**: A simple interface for researchers to write compression, token dropping, and custom serialization through a flexible SERDE interface.
+- **可插拔 KV 转换**：为研究人员提供简洁接口，通过灵活的 SERDE 接口实现压缩、token 丢弃与自定义序列化。

-LMCache is becoming an integral layer in the LLM inference *ecosystem*, with *community*-driven integration with serving engines, inference frameworks, hardware vendors, storage systems, and infrastructure providers:
+LMCache 正成为 LLM 推理 *生态* 中的关键层，并与 serving engine、推理框架、硬件厂商、存储系统及基础设施提供商开展由 *社区* 驱动的集成：

 <p align="center">
  <img src="asset/ecosystem.png" alt="LMCache ecosystem">
 </p>

-## Getting Started
+## 快速开始

-To use LMCache, simply install `lmcache` from your package manager, e.g. pip:
+要使用 LMCache，只需通过包管理器安装 `lmcache`，例如 pip：
 ```bash
 pip install lmcache
 ```

-For more setup options and examples, see:
- [Installation](https://docs.lmcache.ai/getting_started/installation.html)
- [Quickstart](https://docs.lmcache.ai/getting_started/quickstart.html)
+更多安装选项与示例，请参阅：
+- [安装](https://docs.lmcache.ai/getting_started/installation.html)
+- [快速入门](https://docs.lmcache.ai/getting_started/quickstart.html)
 - [LMCache Recipes](https://docs.lmcache.ai/recipes/index.html)
- [CLI Reference](https://docs.lmcache.ai/cli/index.html)
- [Benchmarking Guide](https://docs.lmcache.ai/getting_started/benchmarking.html)
- [Production Deployment](https://docs.lmcache.ai/mp/deployment.html)
+- [CLI 参考](https://docs.lmcache.ai/cli/index.html)
+- [基准测试指南](https://docs.lmcache.ai/getting_started/benchmarking.html)
+- [生产部署](https://docs.lmcache.ai/mp/deployment.html)

-## Contributing
-We welcome and value contributions and collaborations. Join us in improving LMCache. Check out the [Contributing Guide](https://docs.lmcache.ai/developer_guide/contributing.html) or join our [Slack community](https://join.slack.com/t/lmcacheworkspace/shared_invite/zt-3zxjao8h0-lRfBfnLqbALOtLsWn2ITxA) to get started.
+## 贡献
+我们欢迎并重视各类贡献与合作。欢迎加入我们一起改进 LMCache。请查阅[贡献指南](https://docs.lmcache.ai/developer_guide/contributing.html)，或加入我们的 [Slack 社区](https://join.slack.com/t/lmcacheworkspace/shared_invite/zt-3zxjao8h0-lRfBfnLqbALOtLsWn2ITxA) 以开始参与。

-## Adoption and Partnerships
-LMCache has a growing community of developers, researchers, industry adopters, and partners building the next generation of efficient LLM inference systems.
+## 采用与合作伙伴
+LMCache 拥有一个不断壮大的开发者、研究人员、行业采用方和合作伙伴社区，共同构建下一代高效 LLM 推理系统。

 <p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="asset/partner_dark.png">
    <source media="(prefers-color-scheme: light)" srcset="asset/partner_light.png">
-    <img alt="LMCache Adoption and Partnerships" src="asset/partner_light.png">
+    <img alt="LMCache 采用与合作伙伴" src="asset/partner_light.png">
  </picture>
 </p>

-As an independent open-source project, LMCache is becoming the de-facto standard for KV Cache management in LLM inference. Its continued development and community work are supported in part by [Tensormesh](https://www.tensormesh.ai/).
+作为一个独立的开源项目，LMCache 正在成为 LLM 推理中 KV Cache 管理的事实标准。其持续开发和社区工作部分由 [Tensormesh](https://www.tensormesh.ai/). 支持。

-## Citation
+## 引用

-LMCache builds on research in KV cache management, including cache reuse, offloading, compression, and serving optimization. If you use LMCache in your research, please cite the LMCache paper and related work.
+LMCache 建立在 KV cache 管理相关研究之上，包括缓存复用、卸载、压缩和服务优化。如果您在研究中使用 LMCache，请引用 LMCache 论文及相关工作。

 ~~~bibtex
@article{cheng2025lmcache,
@@ -126,7 +132,7 @@ LMCache builds on research in KV cache management, including cache reuse, offloa
 ~~~

 <details>
-<summary>Related papers</summary>
+<summary>相关论文</summary>

 ~~~bibtex
@inproceedings{liu2024cachegen,
@@ -148,6 +154,6 @@ LMCache builds on research in KV cache management, including cache reuse, offloa

 </details>

-## License
+## 许可证

-The LMCache codebase is licensed under Apache License 2.0. See the [LICENSE](LICENSE) file for details.
+LMCache 代码库采用 Apache License 2.0 许可证。详情请参阅 [LICENSE](LICENSE) 文件。