一个仓库,用于在单个消费级GPU(32GB显存)上运行Kimi-Linear-48B模型,并通过协议适配器('k3')连接Claude Code、Codex、Aider等编码代理。

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Apache-2.0

最近更新

2026-07-29

AI 仓库情报摘要
FR-AI / ANALYSIS

为什么值得关注

它在32GB显存限制下,在单个L40S上实现了113.83 tok/s,使用了一个能透明检测和翻译多种客户端协议的适配器,并提供了透明的基准测试性能和详细的内核优化。

适合谁使用

  • 希望无需API成本即可本地运行大型模型的AI开发者
  • 寻求本地推理的编码代理(Claude Code, Codex, Cline, Aider)用户
  • 研究消费级GPU高效推理的研究人员
  • 评估LLM本地部署的企业

典型使用场景

  • 使用本地Kimi模型运行编码代理,保护隐私且无需按token计费
  • 在单个GPU上对推理内核优化进行基准测试和比较
  • 作为在消费级硬件上部署48B模型的参考
  • 测试不同LLM API格式之间的协议转换

项目优势

  • 在L40S上比融合INT4内核提升3.18倍吞吐量(113.83 vs 35.76 tok/s)
  • 实测代理延迟开销仅为每请求0.231毫秒
  • 所有测量均指定硬件并附带可复现的运行脚本
  • 已验证与五种编码代理工具兼容,无需修改客户端

使用前须知

  • 需要32GB显存;24GB显卡支持正在开发中
  • 代理本身不提供模型推理——需要已有的OpenAI兼容后端
  • 完整的Kimi K3(2.78万亿参数)不适用于消费级硬件;需要商业化的8×B300软件包

README 快速开始

local-kimi

Sketch is illustrative. This repository serves Kimi-Linear-48B, measured on an NVIDIA L40S inside a hard 32 GiB cap. The full Kimi K3 is 2.78T parameters and runs on an 8x B300 node, linked below.

TL;DR. Run Kimi on your own consumer GPU, not someone's API. A single 32 GB card (RTX 5090, or any datacenter card) serves Kimi-Linear-48B at 113.83 tok/s, up 3.18x from fused INT4 kernels, and Claude Code, Codex, Cline, Aider and opencode all connect to it unchanged.

Need the full Kimi K3 in production? 2.8 trillion parameters will not fit a workstation. If you want it running inside your own network instead of behind a closed-source API, RunInfra ships it as a deployable package with a pinned vLLM build, a benchmark receipt and weight verification: Kimi K3 on 8x B300 →

Point any coding agent at a local Kimi model and it works: k3 translates between the agent's protocol and the model's.

flowchart LR
    CC["Claude Code"] -- "Anthropic Messages" --> K3
    CX["Codex"] -- "OpenAI Responses" --> K3
    OT["Aider, Cline, opencode"] -- "OpenAI Chat" --> K3

    K3{{"k3detects the client per requesttranslates all three dialectstool calls: hermes, json, kimi,kimi_k3, pythonic, passthroughrestores reasoning across turns"}}

    K3 -- "OpenAI Chat" --> EN["local engineKimi-Linear-48Bselective INT4fused decode kernels"]

    classDef client fill:#0d1117,stroke:#30363d,color:#c9d1d9
    classDef bridge fill:#1f6feb,stroke:#388bfd,color:#ffffff
    classDef engine fill:#238636,stroke:#2ea043,color:#ffffff
    class CC,CX,OT client
    class K3 bridge
    class EN engine

The problem

Local model servers usually speak OpenAI Chat Completions. Claude Code speaks Anthropic Messages. Codex speaks OpenAI Responses. Point the wrong client at the wrong endpoint and the request fails. k3 sits between them, detects the caller on each request, and translates the request and response.

Protocol support

The protocol landscape has changed. Current releases of vLLM, llama.cpp, and [Ollama](https://docs.o

项目描述

Optimized local serving engine for Kimi-Linear-48B: INT4 quantizer, fused decode kernels for a measured 3.18x, and an OpenAI-compatible server. Ships with k3, a bridge that detects the client per request so Claude Code, Codex, Cline, Aider and opencode all work unchanged.

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