MNN 3.4.0 版本发布说明

发布日期: 2026年2月

📌 版本概述

MNN 3.4.0 版本聚焦于 GPU/QNN 后端能力深化、Attention 计算及长文本内存优化 与 GPU 线上运行稳定性 三大核心主题：

GPU/QNN 能力深化: Vulkan 后端新增 LLM 推理支持并引入 CoopMat 矩阵加速指令；Metal 后端支持 TensorAPI 和 Flash Attention；QNN 后端扩展支持 Qwen3 系列和 VL 模型，并新增 Python 直接导出和 OmniQuant 量化能力。
Attention 与长文本内存优化: CPU 和 Metal 后端全面支持 Flash Attention；CPU 支持 KV Cache 量化；新增 Prefix KV Cache 支持；新增统一的 attention_mode 配置选项，为长文本场景显著降低内存占用。
GPU 线上运行稳定性: 新增 iOS 后台检测机制，当 APP 切到后台时 GPU 计算会被系统拒绝，现在会正确返回错误码；修复多个 GPU 后端的稳定性问题。

🚀 版本亮点

Vulkan LLM 支持: Vulkan 后端新增 LLM 推理支持，覆盖更多 Android 设备
Vulkan CoopMat 加速: Vulkan 支持协作矩阵 (CoopMat) 指令，大幅加速矩阵乘法运算
Metal TensorAPI 支持: Metal 后端支持 TensorAPI，M5 芯片性能大幅提升
Metal Flash Attention: Metal 后端实现 Flash Attention，显著降低内存占用
CPU Flash Attention: CPU 后端支持 Flash Attention，新增 attention_mode 统一配置选项
CPU KV Cache 量化: CPU Attention 支持 KV Cache 量化，降低内存占用
Prefix KV Cache: 支持 Prefix KV Cache，提升长文本推理效率
投机解码增强: Metal 支持 Eagle3 和 Lookahead 投机解码；提供 Qwen3 系列 Eagle3 权重
QNN 增强: QNN 后端支持 Qwen3 系列和 VL 模型；支持 Python 直接导出；支持 OmniQuant 量化
混合精度量化: llmexport 支持通过配置文件进行混合精度量化
llmexport 重构: 重构模型导出逻辑，模型抽象更加完善
Loop 算子 GPU 优化: OpenCL 和 Metal 优化 Loop 算子，支持纯 GPU 实现无需回退 CPU
RISC-V Vector (RVV) 优化: 全面使用向量内置函数优化核心算子
Vulkan Buffer FP16: Vulkan buffer 模式完整支持 FP16 计算路径
KleidiAI 集成: 新增编译选项，默认启用 KleidiAI fp32 深度卷积内核

✨ 新功能

LLM/VLM

Flash Attention: CPU 和 Metal 后端支持 Flash Attention，Metal 支持三种模式可选
KV Cache 量化: CPU Attention 支持 Query/Key/Value 的 8bit 非对称量化
Prefix KV Cache: 支持 Prefix KV Cache，提升长文本推理效率
投机解码: Metal 后端支持 Eagle3 和 Lookahead 投机解码；提供 Qwen3 系列 Eagle3 权重
llmexport 重构: 重构模型导出逻辑，支持混合精度量化配置和无权重导出
新增 attention_mode 选项: 统一配置 Attention 行为，废弃 quant_qkv 选项
- CPU: 0, 1, 2, 8, 9, 10（默认 8），控制 Flash Attention 和 QKV 量化
- GPU (Metal): 0, 8, 16（默认 8），控制 Flash Attention 实现方式
新增 Fun-Audio-Chat-8B 模型支持；LoRA 支持克隆 LayerNorm；llm_bench 支持 JSON 输出

GPU、QNN 后端

Vulkan: 新增 LLM 推理支持；CoopMat 矩阵加速；buffer 模式完整支持 FP16
Metal: TensorAPI 支持（M5 芯片性能提升）；iOS 后台检测机制；Loop 算子优化
OpenCL: Loop 算子纯 GPU 实现；mmap 权重存储
QNN: 支持 Qwen3 系列和 VL 模型；Python 直接导出；OmniQuant 量化

工具与应用

MNNConvert: 新增 dump pass 和 ConvPad 融合优化
mnncli 重构: 增加测试、Linux 构建、QNN 库下载
新增 Supertonic TTS、Sana diffusion、Sherpa-MNN TTS Demo 支持
MNN Chat (Android): 模型市场优化、多图片输入、Debug 工具

⚡ 性能优化

CPU: LayerNorm/MatMul/二元广播优化；线程池开销降低；Attention Softmax 缓存优化
RISC-V Vector: 全面优化 pack/unpack、转置、数学、卷积、Softmax、CV 等核心函数
KleidiAI: 新增编译选项默认启用 fp32 深度卷积内核

🐛 缺陷修复

核心: 修复 axis() 返回 nullptr 崩溃；新增 _SSE_MNNSoftmax 修复 AVX2 禁用时崩溃
GPU: 修复 iOS LLM 后台执行报错；修复 OpenCL/Vulkan/Metal 多个计算和稳定性问题
LLM: 修复 FastVLM/Eagle 导出问题；修复 CPU Attention 量化溢出
工具: 修复 MNN2QNNModel、diffusion 导出、mnncli 等问题

📚 其他改进

文档: 新增 NPU 导出和 Attention 量化参数文档
CI: 新增 GitHub/内部代码自动同步脚本；SmolVLM 测试；LLM 夜间测试
构建: 默认启用 KleidiAI；RVV 宏跨平台支持；Linux mnncli 构建

🙏 致谢

衷心感谢所有外部贡献者对本版本的宝贵贡献：

@ihb2032 - 全面的 RISC-V Vector (RVV) 优化
@jxt1234 - 性能优化与缺陷修复
@HenryDen - KleidiAI 集成
@Juude - TTS/App 优化与 mnncli 重构
@vra - Supertonic TTS 支持
@zlaazlaa - Diffusion 导出修复
@bolun365 - MNN 库构建脚本
@JunKnows - OpenCL/Vulkan 修复
@rainyl - CMake 改进
@LudovicoYIN - 量化工具修复
@codefuturedalao - 代码优化
@EricMoin - 变量初始化修复
@Edward-Elric233 - MinGW 构建支持
@AliasJeff - 文档和 CLI 改进
@jules-ai - 文档修正
@vvverily - App 文案修正

📦 不兼容变更

quant_qkv 选项已废弃，请使用 attention_mode

完整变更日志: https://github.com/alibaba/MNN/compare/3.3.0...3.4.0

MNN 3.4.0 Release Notes

Release Date: February 2026

📌 Overview

MNN 3.4.0 focuses on three core themes: Deepening GPU/QNN Backend Capabilities, Optimizing Attention Computation & Long-Context Memory Usage, and GPU Runtime Stability:

GPU/QNN Capability Enhancement: Vulkan backend adds LLM inference support with CoopMat matrix acceleration; Metal backend supports TensorAPI and Flash Attention; QNN backend extends to support Qwen3 series and VL models with direct Python export and OmniQuant quantization.
Attention & Long-Context Memory Optimization: CPU and Metal backends fully support Flash Attention; CPU supports KV Cache quantization; Prefix KV Cache support added; new unified attention_mode configuration option significantly reduces memory usage for long-context scenarios.
GPU Runtime Stability: Added iOS background detection mechanism - when app goes to background, GPU computation is rejected by the system, now correctly returns error code; fixed multiple GPU backend stability issues.

🚀 Highlights

Vulkan LLM Support: Vulkan backend now supports LLM inference, extending coverage to more Android devices
Vulkan CoopMat Acceleration: Vulkan supports Cooperative Matrix (CoopMat) instructions for significantly faster matrix multiplication
Metal TensorAPI Support: Metal backend supports TensorAPI, significant performance boost on M5 chips
Metal Flash Attention: Metal backend implements Flash Attention with significantly reduced memory usage
CPU Flash Attention: CPU backend supports Flash Attention with new unified attention_mode option
CPU KV Cache Quantization: CPU Attention supports KV Cache quantization to reduce memory usage
Prefix KV Cache: Support for Prefix KV Cache, improving long-text inference efficiency
Speculative Decoding Enhancement: Metal supports Eagle3 and Lookahead speculative decoding; Qwen3 Eagle3 weights provided
QNN Enhancement: QNN backend supports Qwen3 series and VL models; direct Python export; OmniQuant quantization support
Mixed Precision Quantization: llmexport supports mixed precision quantization via config file
llmexport Refactoring: Refactored model export logic with improved model abstraction
Loop Op GPU Optimization: OpenCL and Metal optimize Loop operator, pure GPU implementation without CPU fallback
RISC-V Vector (RVV) Optimization: Comprehensive intrinsic optimization across core operations
Vulkan Buffer FP16: Full FP16 compute path for Vulkan buffer mode
KleidiAI Integration: Added compile option to enable KleidiAI fp32 depth-wise kernels by default

✨ New Features

LLM/VLM

Flash Attention: CPU and Metal backends support Flash Attention, Metal with three selectable modes
KV Cache Quantization: CPU Attention supports 8-bit asymmetric quantization for Query/Key/Value
Prefix KV Cache: Support for Prefix KV Cache, improving long-text inference efficiency
Speculative Decoding: Metal supports Eagle3 and Lookahead; Qwen3 Eagle3 weights provided
llmexport Refactoring: Refactored model export logic, supports mixed precision quantization and weight-free export
New attention_mode Option: Unified configuration for Attention behavior, deprecates quant_qkv
- CPU: 0, 1, 2, 8, 9, 10 (default 8), controls Flash Attention and QKV quantization
- GPU (Metal): 0, 8, 16 (default 8), controls Flash Attention implementation
Added Fun-Audio-Chat-8B support; LoRA supports cloning LayerNorm; llm_bench supports JSON output

GPU/QNN Backends

Vulkan: Added LLM inference support; CoopMat matrix acceleration; full FP16 buffer mode
Metal: TensorAPI support (M5 chip performance boost); iOS background detection; Loop op optimization
OpenCL: Pure GPU Loop operator implementation; mmap weight storage
QNN: Supports Qwen3 series and VL models; direct Python export; OmniQuant quantization

Tools & Apps

MNNConvert: Added dump pass and ConvPad fusion optimization
mnncli refactoring: Added tests, Linux build, QNN library download
Added Supertonic TTS, Sana diffusion, Sherpa-MNN TTS Demo support
MNN Chat (Android): Model market optimization, multi-image input, Debug tools

⚡ Performance Optimizations

CPU: LayerNorm/MatMul/binary broadcast optimization; ThreadPool overhead reduction; Attention Softmax cache optimization
RISC-V Vector: Comprehensive optimization for pack/unpack, transpose, math, conv, Softmax, CV functions
KleidiAI: Added compile option to enable fp32 depth-wise kernels by default

🐛 Bug Fixes

Core: Fixed axis() nullptr crash; added _SSE_MNNSoftmax for AVX2 disabled crash
GPU: Fixed iOS LLM background execution error; fixed multiple OpenCL/Vulkan/Metal computation and stability issues
LLM: Fixed FastVLM/Eagle export issues; fixed CPU Attention quant overflow
Tools: Fixed MNN2QNNModel, diffusion export, mnncli issues

📚 Other Improvements

Documentation: Added NPU export and Attention quantization parameter docs
CI: Added GitHub/internal code auto-sync script; SmolVLM test; LLM nightly test
Build: KleidiAI enabled by default; RVV macro cross-platform support; Linux mnncli build

🙏 Acknowledgements

We sincerely thank all external contributors for their valuable contributions to this release:

@ihb2032 - Comprehensive RISC-V Vector (RVV) optimization
@jxt1234 - Performance optimizations and bug fixes
@HenryDen - KleidiAI integration
@Juude - TTS/App optimization and mnncli refactoring
@vra - Supertonic TTS support
@zlaazlaa - Diffusion export fixes
@bolun365 - MNN library build script
@JunKnows - OpenCL/Vulkan fixes
@rainyl - CMake improvements
@LudovicoYIN - Quantization tool fixes
@codefuturedalao - Code optimizations
@EricMoin - Variable initialization fixes
@Edward-Elric233 - MinGW build support
@AliasJeff - Documentation and CLI improvements
@jules-ai - Typo corrections
@vvverily - App text corrections

📦 Breaking Changes

quant_qkv option is deprecated, please use attention_mode instead

Full Changelog: https://github.com/alibaba/MNN/compare/3.3.0...3.4.0

本文由 Claude Opus 4.5 协助生成 / This document was generated with assistance from Claude Opus 4.5

MNN 3.4.0 版本发布说明

发布日期: 2026年2月

📌 版本概述

MNN 3.4.0 版本聚焦于 GPU/QNN 后端能力深化、Attention 计算及长文本内存优化 与 GPU 线上运行稳定性 三大核心主题：

GPU/QNN 能力深化: Vulkan 后端新增 LLM 推理支持并引入 CoopMat 矩阵加速指令；Metal 后端支持 TensorAPI 和 Flash Attention；QNN 后端扩展支持 Qwen3 系列和 VL 模型，并新增 Python 直接导出和 OmniQuant 量化能力。
Attention 与长文本内存优化: CPU 和 Metal 后端全面支持 Flash Attention；CPU 支持 KV Cache 量化；新增 Prefix KV Cache 支持；新增统一的 attention_mode 配置选项，为长文本场景显著降低内存占用。
GPU 线上运行稳定性: 新增 iOS 后台检测机制，当 APP 切到后台时 GPU 计算会被系统拒绝，现在会正确返回错误码；修复多个 GPU 后端的稳定性问题。

🚀 版本亮点

Vulkan LLM 支持: Vulkan 后端新增 LLM 推理支持，覆盖更多 Android 设备
Vulkan CoopMat 加速: Vulkan 支持协作矩阵 (CoopMat) 指令，大幅加速矩阵乘法运算
Metal TensorAPI 支持: Metal 后端支持 TensorAPI，M5 芯片性能大幅提升
Metal Flash Attention: Metal 后端实现 Flash Attention，显著降低内存占用
CPU Flash Attention: CPU 后端支持 Flash Attention，新增 attention_mode 统一配置选项
CPU KV Cache 量化: CPU Attention 支持 KV Cache 量化，降低内存占用
Prefix KV Cache: 支持 Prefix KV Cache，提升长文本推理效率
投机解码增强: Metal 支持 Eagle3 和 Lookahead 投机解码；提供 Qwen3 系列 Eagle3 权重
QNN 增强: QNN 后端支持 Qwen3 系列和 VL 模型；支持 Python 直接导出；支持 OmniQuant 量化
混合精度量化: llmexport 支持通过配置文件进行混合精度量化
llmexport 重构: 重构模型导出逻辑，模型抽象更加完善
Loop 算子 GPU 优化: OpenCL 和 Metal 优化 Loop 算子，支持纯 GPU 实现无需回退 CPU
RISC-V Vector (RVV) 优化: 全面使用向量内置函数优化核心算子
Vulkan Buffer FP16: Vulkan buffer 模式完整支持 FP16 计算路径
KleidiAI 集成: 新增编译选项，默认启用 KleidiAI fp32 深度卷积内核

✨ 新功能

LLM/VLM

Flash Attention: CPU 和 Metal 后端支持 Flash Attention，Metal 支持三种模式可选
KV Cache 量化: CPU Attention 支持 Query/Key/Value 的 8bit 非对称量化
Prefix KV Cache: 支持 Prefix KV Cache，提升长文本推理效率
投机解码: Metal 后端支持 Eagle3 和 Lookahead 投机解码；提供 Qwen3 系列 Eagle3 权重
llmexport 重构: 重构模型导出逻辑，支持混合精度量化配置和无权重导出
新增 attention_mode 选项: 统一配置 Attention 行为，废弃 quant_qkv 选项
- CPU: 0, 1, 2, 8, 9, 10（默认 8），控制 Flash Attention 和 QKV 量化
- GPU (Metal): 0, 8, 16（默认 8），控制 Flash Attention 实现方式
新增 Fun-Audio-Chat-8B 模型支持；LoRA 支持克隆 LayerNorm；llm_bench 支持 JSON 输出

GPU、QNN 后端

Vulkan: 新增 LLM 推理支持；CoopMat 矩阵加速；buffer 模式完整支持 FP16
Metal: TensorAPI 支持（M5 芯片性能提升）；iOS 后台检测机制；Loop 算子优化
OpenCL: Loop 算子纯 GPU 实现；mmap 权重存储
QNN: 支持 Qwen3 系列和 VL 模型；Python 直接导出；OmniQuant 量化

工具与应用

MNNConvert: 新增 dump pass 和 ConvPad 融合优化
mnncli 重构: 增加测试、Linux 构建、QNN 库下载
新增 Supertonic TTS、Sana diffusion、Sherpa-MNN TTS Demo 支持
MNN Chat (Android): 模型市场优化、多图片输入、Debug 工具

⚡ 性能优化

CPU: LayerNorm/MatMul/二元广播优化；线程池开销降低；Attention Softmax 缓存优化
RISC-V Vector: 全面优化 pack/unpack、转置、数学、卷积、Softmax、CV 等核心函数
KleidiAI: 新增编译选项默认启用 fp32 深度卷积内核

🐛 缺陷修复

核心: 修复 axis() 返回 nullptr 崩溃；新增 _SSE_MNNSoftmax 修复 AVX2 禁用时崩溃
GPU: 修复 iOS LLM 后台执行报错；修复 OpenCL/Vulkan/Metal 多个计算和稳定性问题
LLM: 修复 FastVLM/Eagle 导出问题；修复 CPU Attention 量化溢出
工具: 修复 MNN2QNNModel、diffusion 导出、mnncli 等问题

📚 其他改进

文档: 新增 NPU 导出和 Attention 量化参数文档
CI: 新增 GitHub/内部代码自动同步脚本；SmolVLM 测试；LLM 夜间测试
构建: 默认启用 KleidiAI；RVV 宏跨平台支持；Linux mnncli 构建

🙏 致谢

衷心感谢所有外部贡献者对本版本的宝贵贡献：

@ihb2032 - 全面的 RISC-V Vector (RVV) 优化
@jxt1234 - 性能优化与缺陷修复
@HenryDen - KleidiAI 集成
@Juude - TTS/App 优化与 mnncli 重构
@vra - Supertonic TTS 支持
@zlaazlaa - Diffusion 导出修复
@bolun365 - MNN 库构建脚本
@JunKnows - OpenCL/Vulkan 修复
@rainyl - CMake 改进
@LudovicoYIN - 量化工具修复
@codefuturedalao - 代码优化
@EricMoin - 变量初始化修复
@Edward-Elric233 - MinGW 构建支持
@AliasJeff - 文档和 CLI 改进
@jules-ai - 文档修正
@vvverily - App 文案修正

📦 不兼容变更

quant_qkv 选项已废弃，请使用 attention_mode

完整变更日志: https://github.com/alibaba/MNN/compare/3.3.0...3.4.0

MNN 3.4.0 Release Notes

Release Date: February 2026

📌 Overview

MNN 3.4.0 focuses on three core themes: Deepening GPU/QNN Backend Capabilities, Optimizing Attention Computation & Long-Context Memory Usage, and GPU Runtime Stability:

GPU/QNN Capability Enhancement: Vulkan backend adds LLM inference support with CoopMat matrix acceleration; Metal backend supports TensorAPI and Flash Attention; QNN backend extends to support Qwen3 series and VL models with direct Python export and OmniQuant quantization.
Attention & Long-Context Memory Optimization: CPU and Metal backends fully support Flash Attention; CPU supports KV Cache quantization; Prefix KV Cache support added; new unified attention_mode configuration option significantly reduces memory usage for long-context scenarios.
GPU Runtime Stability: Added iOS background detection mechanism - when app goes to background, GPU computation is rejected by the system, now correctly returns error code; fixed multiple GPU backend stability issues.

🚀 Highlights

Vulkan LLM Support: Vulkan backend now supports LLM inference, extending coverage to more Android devices
Vulkan CoopMat Acceleration: Vulkan supports Cooperative Matrix (CoopMat) instructions for significantly faster matrix multiplication
Metal TensorAPI Support: Metal backend supports TensorAPI, significant performance boost on M5 chips
Metal Flash Attention: Metal backend implements Flash Attention with significantly reduced memory usage
CPU Flash Attention: CPU backend supports Flash Attention with new unified attention_mode option
CPU KV Cache Quantization: CPU Attention supports KV Cache quantization to reduce memory usage
Prefix KV Cache: Support for Prefix KV Cache, improving long-text inference efficiency
Speculative Decoding Enhancement: Metal supports Eagle3 and Lookahead speculative decoding; Qwen3 Eagle3 weights provided
QNN Enhancement: QNN backend supports Qwen3 series and VL models; direct Python export; OmniQuant quantization support
Mixed Precision Quantization: llmexport supports mixed precision quantization via config file
llmexport Refactoring: Refactored model export logic with improved model abstraction
Loop Op GPU Optimization: OpenCL and Metal optimize Loop operator, pure GPU implementation without CPU fallback
RISC-V Vector (RVV) Optimization: Comprehensive intrinsic optimization across core operations
Vulkan Buffer FP16: Full FP16 compute path for Vulkan buffer mode
KleidiAI Integration: Added compile option to enable KleidiAI fp32 depth-wise kernels by default

✨ New Features

LLM/VLM

Flash Attention: CPU and Metal backends support Flash Attention, Metal with three selectable modes
KV Cache Quantization: CPU Attention supports 8-bit asymmetric quantization for Query/Key/Value
Prefix KV Cache: Support for Prefix KV Cache, improving long-text inference efficiency
Speculative Decoding: Metal supports Eagle3 and Lookahead; Qwen3 Eagle3 weights provided
llmexport Refactoring: Refactored model export logic, supports mixed precision quantization and weight-free export
New attention_mode Option: Unified configuration for Attention behavior, deprecates quant_qkv
- CPU: 0, 1, 2, 8, 9, 10 (default 8), controls Flash Attention and QKV quantization
- GPU (Metal): 0, 8, 16 (default 8), controls Flash Attention implementation
Added Fun-Audio-Chat-8B support; LoRA supports cloning LayerNorm; llm_bench supports JSON output

GPU/QNN Backends

Vulkan: Added LLM inference support; CoopMat matrix acceleration; full FP16 buffer mode
Metal: TensorAPI support (M5 chip performance boost); iOS background detection; Loop op optimization
OpenCL: Pure GPU Loop operator implementation; mmap weight storage
QNN: Supports Qwen3 series and VL models; direct Python export; OmniQuant quantization

Tools & Apps

MNNConvert: Added dump pass and ConvPad fusion optimization
mnncli refactoring: Added tests, Linux build, QNN library download
Added Supertonic TTS, Sana diffusion, Sherpa-MNN TTS Demo support
MNN Chat (Android): Model market optimization, multi-image input, Debug tools

⚡ Performance Optimizations

CPU: LayerNorm/MatMul/binary broadcast optimization; ThreadPool overhead reduction; Attention Softmax cache optimization
RISC-V Vector: Comprehensive optimization for pack/unpack, transpose, math, conv, Softmax, CV functions
KleidiAI: Added compile option to enable fp32 depth-wise kernels by default

🐛 Bug Fixes

Core: Fixed axis() nullptr crash; added _SSE_MNNSoftmax for AVX2 disabled crash
GPU: Fixed iOS LLM background execution error; fixed multiple OpenCL/Vulkan/Metal computation and stability issues
LLM: Fixed FastVLM/Eagle export issues; fixed CPU Attention quant overflow
Tools: Fixed MNN2QNNModel, diffusion export, mnncli issues

📚 Other Improvements

Documentation: Added NPU export and Attention quantization parameter docs
CI: Added GitHub/internal code auto-sync script; SmolVLM test; LLM nightly test
Build: KleidiAI enabled by default; RVV macro cross-platform support; Linux mnncli build

🙏 Acknowledgements

We sincerely thank all external contributors for their valuable contributions to this release:

@ihb2032 - Comprehensive RISC-V Vector (RVV) optimization
@jxt1234 - Performance optimizations and bug fixes
@HenryDen - KleidiAI integration
@Juude - TTS/App optimization and mnncli refactoring
@vra - Supertonic TTS support
@zlaazlaa - Diffusion export fixes
@bolun365 - MNN library build script
@JunKnows - OpenCL/Vulkan fixes
@rainyl - CMake improvements
@LudovicoYIN - Quantization tool fixes
@codefuturedalao - Code optimizations
@EricMoin - Variable initialization fixes
@Edward-Elric233 - MinGW build support
@AliasJeff - Documentation and CLI improvements
@jules-ai - Typo corrections
@vvverily - App text corrections

📦 Breaking Changes

quant_qkv option is deprecated, please use attention_mode instead

Full Changelog: https://github.com/alibaba/MNN/compare/3.3.0...3.4.0

本文由 Claude Opus 4.5 协助生成 / This document was generated with assistance from Claude Opus 4.5

MNN 3.4.0 版本发布说明

📌 版本概述

🚀 版本亮点

✨ 新功能

LLM/VLM

GPU、QNN 后端

工具与应用

⚡ 性能优化

🐛 缺陷修复

📚 其他改进

🙏 致谢

📦 不兼容变更

MNN 3.4.0 Release Notes

📌 Overview

🚀 Highlights

✨ New Features

LLM/VLM

GPU/QNN Backends

Tools & Apps

⚡ Performance Optimizations

🐛 Bug Fixes

📚 Other Improvements

🙏 Acknowledgements

📦 Breaking Changes

More C++ Projects

tensorflow

electron

llama.cpp

godot

MNN 3.4.0 版本发布说明

📌 版本概述

🚀 版本亮点

✨ 新功能

LLM/VLM

GPU、QNN 后端

工具与应用

⚡ 性能优化

🐛 缺陷修复

📚 其他改进

🙏 致谢

📦 不兼容变更

MNN 3.4.0 Release Notes

📌 Overview

🚀 Highlights

✨ New Features

LLM/VLM

GPU/QNN Backends

Tools & Apps

⚡ Performance Optimizations

🐛 Bug Fixes

📚 Other Improvements

🙏 Acknowledgements

📦 Breaking Changes

More C++ Projects

tensorflow

electron

llama.cpp

godot