惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

T
The Exploit Database - CXSecurity.com
S
Secure Thoughts
A
Arctic Wolf
V
Vulnerabilities – Threatpost
S
Schneier on Security
D
Darknet – Hacking Tools, Hacker News & Cyber Security
T
Threat Research - Cisco Blogs
AWS News Blog
AWS News Blog
NISL@THU
NISL@THU
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
P
Palo Alto Networks Blog
L
Lohrmann on Cybersecurity
Schneier on Security
Schneier on Security
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Scott Helme
Scott Helme
L
LINUX DO - 最新话题
L
LangChain Blog
量子位
T
Threatpost
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
腾讯CDC
W
WeLiveSecurity
Last Week in AI
Last Week in AI
美团技术团队
The GitHub Blog
The GitHub Blog
The Last Watchdog
The Last Watchdog
C
CERT Recently Published Vulnerability Notes
月光博客
月光博客
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
L
LINUX DO - 热门话题
Microsoft Security Blog
Microsoft Security Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Troy Hunt's Blog
Webroot Blog
Webroot Blog
云风的 BLOG
云风的 BLOG
博客园 - 叶小钗
V2EX - 技术
V2EX - 技术
雷峰网
雷峰网
Security Latest
Security Latest
小众软件
小众软件
J
Java Code Geeks
博客园 - Franky
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Spread Privacy
Spread Privacy
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
Recent Commits to openclaw:main
Recent Commits to openclaw:main
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
T
The Blog of Author Tim Ferriss
M
MIT News - Artificial intelligence

博客园 - DHclly

Aspose.Words 合并单元格的原理、配置方式、代码示例以及常见误区 Gpustack 运行一段时间后出现 Failed to initialize NVML: Unknown Error 解决办法 wps dispimg python 解析实现参考 Amazon S3 Tools:S3cmd 介绍 wsl 和win主机互相访问 nginx 根路径同时代理 http ws sse 三种请求 在 X86_64(amd64) 平台上的docker支持打包跨平台的镜像(如arm64) 以图搜图功能介绍 docker 容器调试技巧 open ai sdk 的额外请求头说明 x-stainless 大模型常见的概念 创建软连接的几种方式 基于node.js 的 web server 实现 对个人的警醒 jQuery对象与DOM对象之间的转换方法 【BUG】浏览器控制台提示:net::ERR_INVALID_CHUNKED_ENCODING 200 (OK) 的解决思路 实用浏览器脚本 关于Lambda表达式(箭头函数)的get属性访问器和常规的get属性访问器的差异 转换字符串为二进制编码字符串
NVIDIA GPU 计算能力( compute capability,SM version)兼容性查询
DHclly · 2025-04-21 · via 博客园 - DHclly

NVIDIA GPU 兼容性查询地址:

CUDA GPUs - Compute Capability | NVIDIA Developer(https://developer.nvidia.com/cuda-gpus)

1. Introduction — CUDA C++ Programming Guide(https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability)

The compute capability of a device is represented by a version number, also sometimes called its “SM version”. This version number identifies the features supported by the GPU hardware and is used by applications at runtime to determine which hardware features and/or instructions are available on the present GPU.
设备的计算能力由版本号表示,有时也称为其“SM 版本”。此版本号标识 GPU 硬件支持的功能,并由应用程序在运行时用于确定当前 GPU 上可用的硬件功能和/或指令。

The compute capability comprises a major revision number X and a minor revision number Y and is denoted by X.Y.
计算功能包括一个主要修订号 X 和一个次要修订号 Y,用 X.Y 表示。

Devices with the same major revision number are of the same core architecture. The major revision number is 9 for devices based on the NVIDIA Hopper GPU architecture, 8 for devices based on the NVIDIA Ampere GPU architecture, 7 for devices based on the Volta architecture, 6 for devices based on the Pascal architecture, 5 for devices based on the Maxwell architecture, and 3 for devices based on the Kepler architecture.
具有相同主要修订号的设备具有相同的内核体系结构。对于基于 NVIDIA Hopper GPU 架构的设备,主要修订号为 9,对于基于 NVIDIA Ampere GPU 架构的设备,8 对于基于 Volta 架构的设备,7 对于基于 Volta 架构的设备,6 对于基于 Pascal 架构的设备,5 对于基于 Maxwell 架构的设备,以及 3 对于基于 Kepler 架构的设备。

The minor revision number corresponds to an incremental improvement to the core architecture, possibly including new features.
次要修订号对应于对核心架构的增量改进,可能包括新功能。

Turing is the architecture for devices of compute capability 7.5, and is an incremental update based on the Volta architecture.
Turing 是计算能力为 7.5 的设备架构,是基于 Volta 架构的增量更新。

CUDA-Enabled GPUs lists of all CUDA-enabled devices along with their compute capability. Compute Capabilities gives the technical specifications of each compute capability.
启用 CUDA 的 GPU 列出所有支持 CUDA 的设备及其计算能力。 计算能力 提供了每种计算能力的技术规格。

The compute capability version of a particular GPU should not be confused with the CUDA version (for example, CUDA 7.5, CUDA 8, CUDA 9), which is the version of the CUDA software platform. The CUDA platform is used by application developers to create applications that run on many generations of GPU architectures, including future GPU architectures yet to be invented. While new versions of the CUDA platform often add native support for a new GPU architecture by supporting the compute capability version of that architecture, new versions of the CUDA platform typically also include software features that are independent of hardware generation.

不应将特定 GPU 的计算能力版本与 CUDA 版本(例如 CUDA 7.5、CUDA 8、CUDA 9)混淆,后者是 CUDA 软件平台的版本。应用程序开发人员使用 CUDA 平台创建在多代 GPU 架构上运行的应用程序,包括尚未发明的未来 GPU 架构。虽然 CUDA 平台的新版本通常会通过支持该架构的计算能力版本来添加对新 GPU 架构的原生支持,但 CUDA 平台的新版本通常还包括独立于硬件代系的软件功能。

The Tesla and Fermi architectures are no longer supported starting with CUDA 7.0 and CUDA 9.0, respectively.
从 CUDA 7.0 和 CUDA 9.0 开始,不再支持 Tesla 和 Fermi 架构。