LocalLLaMA users struggle with model benchmarks
11 posts across 2 communities in the last 60 days; 91% from r/LocalLLaMA.
“"The only model that proved [...]" Well, that's the whole point. With an open and unified benchmarks framework they try to prove some ranking. Your personal use cases can't beat that. Now, you can claim something based on your personal use cases, but, for us, what is the most…”
“We already have a thousand ways to measure model quality, and honestly it just makes things harder. There are already too many benchmarks, so I'm not sure we need another one. What seems to be missing in local AI benchmarking is automatic tuning. Nobody is really focusing on…”
“just run it at high, what are y'all doing that you think you need max thinking so much? luna is best for busy work - your agent filtering your emails, pushing PRs, creating commit messages, doing tasks and such. Sol can code all day long. Astra for when you're stuck or you need…”
Users are questioning the reliability of benchmark results for AI models, particularly when comparing different platforms and configurations. They express concern about discrepancies between cloud-based benchmarks and local performance.
SOURCES (12)
“just run it at high, what are y'all doing that you think you need max thinking so much? luna is best for busy work - your agent filtering your emails, pushing PRs, creating commit messages, doing tasks and such. Sol can code all day long.…”
“I wonder what would a model think of being quantized? Like FP16 version seeing a Q4 version of itself, is it like people seeing their future versions but fat and depressed?”
“There are a bunch of them already and some are very sketchy. I’m going to be honest I’d rather abliterate it at home (if possible) open weight models than download something from the equivalent of “trust me bro, download this off discord””
“"The only model that proved [...]" Well, that's the whole point. With an open and unified benchmarks framework they try to prove some ranking. Your personal use cases can't beat that. Now, you can claim something based on your personal use cases, but, for us, what is the most likely to match our use cases? The many cases that are used by the benchmarks or yours?”
“We already have a thousand ways to measure model quality, and honestly it just makes things harder. There are already too many benchmarks, so I'm not sure we need another one. What seems to be missing in local AI benchmarking is automatic tuning. Nobody is really focusing on finding the best runtime parameters for a given hardware setup, architecture, and quantization. That's a much more practical problem for end users. That's pretty much the gap I'm trying to fill rn”
“...and that's a good use case not some another artificial benchmark :) And another sign that Muse Glimmer is an underdog on r/LocalLLaMA”
“llmfit.org is showing completely out of date stuff like Deepseek R1 https://llmfit.io/ shows no models at all https://llmfitcheck.com/ is down submitted by /u/rm-rf-rm [link] [comments]”
“I really don’t recommend it, the cloud versions of it are very very expensive. E.g. with my usage today, over 4h of cumulative prefill/inference, I spent $0.10 of electricity while for the same reads, cached reads, writes I would have spent $10 on OpenRouter. Just in one day! And the day is not even finished. I mean, you can try it for the experience, but I wouldn’t use it long term.”
“Well, all closed model (and most open weights) benchmarks are run in the cloud, hosted by the same lab, who trained these models. They are not reference for the honesty of their ZDR implementation.”