LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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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.
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Oh, that's quite fancy hardware.
Hmm.. Unless exllama is explicitly recommended by NVIDIA for that particular GPU and setup, it seems "risky". vLLM seems to be the popular choice for most "production" systems. I'm switching from llama.cpp to vLLM because of better performance and its the engine recommended by most model providers. I don't really have the time to benchmark, so I'll just do what the documentation says. And it's really hard to do good benchmarks. Especially when "qualitative language performance" can vary for the same weights on different hardware/software.
With that kind of hardware, I would do exactly what NVIDIA and your model provider(s) say. Otherwise you might waste a lot of GPU power.
Thank you for taking the time to respond.
I've used vLLM for hosting a smaller model which could fit in two of GPUs, it was very performant especially for multiple requests at the same time. The major drawback for my setup was that it only supports tensor parallelism for 2, 4, 8, etc. GPUs and data paralellism slowed inference down considerably, at least for my cards. exllamav3 is the only engine I'm aware of which support 3-way TP.
But I'm fully with you in that vLLM seems to be the most recommended and battle-tested solution.
I might take a look at how I can safely upgrade the driver until I can afford a fourth card and switch back to vLLM.