23
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 01 Nov 2026
23 points (92.6% liked)
LocalLLaMA
5571 readers
132 users here now
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.
As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
founded 3 years ago
MODERATORS
Just to clear up some potential misunderstanding.. Ampere is the Nvidia GPU architecture, and your card is in the same category as RTX30xx. This can matter when it comes to hardware support for llama.cpp and other software.
Having tensor cores affects the speed your card does certain calculations, but AFAIK there are different types of tensor cores in GPUs and if you're using quantized models (which you most likely will be, unless you're training your own models) then it's INT8 performance that matters.
Your amount of VRAM determines how large (which usually correlates to how "intelligent" the model is) model you can fully load on the GPU. If the entire model doesn't fit in VRAM it can use CPU RAM swapping (partial offloading) which severely harms performance, though MoE models are less affected.
So both VRAM and tensor cores affect generation speed, but in completely different ways.
Thank you