创建

登录 ReadmeX

或

了解我们如何处理你的信息: 隐私政策

ReadmeX

陪你读懂 AI 动态

今天,想先了解什么?

选一份动态总结,或直接聊聊你的问题。

社区和关注动态总结只属于你自己。

资讯

社区报告称旧款NVIDIA GPU可解锁张量核心限速

AI 总结

一篇Reddit用户的技术报告称,基于固件的解锁方法可让NVIDIA的Turing、Ampere和Ada GPU在FP32累加时恢复完整张量核心速度,但消费级Blackwell仍受限制。作者将差异归因于出厂熔丝设置和受保护的覆盖寄存器,但现有证据没有独立硬件验证,也没有NVIDIA声明确认其机制或被指称的主观意图。

深度见解

报告称FP32累加常用于AI训练和推理,因此解除相关张量运算限制可能显著改变受影响消费级GPU的性能表现。

Hello guys, hoping you're doing fine. Wall of text next I know, but I want to get this info out of my chest, all hand written lol.

I wanted to make this post explaining some things about GPU unlocks that have been going on lately.

What are these GPU unlocks doing?

NVIDIA on purpose neuters tensor core performance when using FP32 accumulation (FP16, BF16, FP8 and TF32 inputs accumulating in FP32, which is what most AI/LLM/ML tasks use), for market segmentation. Normal FP32 on the CUDA cores (no tensor cores) is NOT affected.

Consumer and prosumer GPUs use the exact same die (like GB202 between the 5090 and 6000 PRO, AD102 between the 4090 and 6000 Ada, or even literally 1:1 with the 3090 Ti vs A6000, both with the same core amounts and 84 SMs).

How it works: the speed of each kind of tensor operation is set by fuses burned at the factory. On consumer cards, the fuse for "FP16/BF16 with FP32 accumulation" says HALF_SPEED, on the pro cards it says FULL_SPEED. These fuses don't cut any circuit, the GPU just reads them at boot and runs at that speed. The GPU also has an override register that can set them back to FULL_SPEED, but it's locked so only NVIDIA's most privileged firmware can write to it, not your PC.

#NVIDIA#GeForce RTX 20 Series#GeForce RTX 30 Series#GeForce RTX 40 Series#GeForce RTX 50 Series#RTX 5090#RTX 6000 PRO

原文约 9 分钟读完阅读原文
群聊

还没有评论,来说说你的看法。