IDK all of my personal and professional projects involve pushing the SOTA to the absolute limit. Using anything other than the latest OpenAI or Anthropic model is out of the question.
Smaller open source models are a bit like 3d printing in the early days; fun to experiment with but really not that valuable for anything other than making toys.
Text summarization, maybe? But even then I want a model that understands the complete context and does a good job. Even things like "generate one sentence about the action we're performing" I usually find I can just incorporate it into the output schema of a larger request instead of making a separate request to a smaller model.
It seems to me like the use case for local GPUs is almost entirely privacy.
If you buy a 15k AUD rtx 6000 96GB, that card will _never_ pay for itself on a gpt-oss:120b workload vs just using openrouter - no matter how many tokens you push through it - because the cost of residential power in Australia means you cannot generate tokens cheaper than the cloud even if the card were free.
> because the cost of residential power in Australia
This so doesn't really matter to your overall point which I agree with but:
The rise of rooftop solar and home battery energy storage flips this a bit now in Australia, IMO. At least where I live, every house has a solar panel on it.
Not worth it just for local LLM usage, but an interesting change to energy economics IMO!
There’s a few more considerations:
- You can use the GPU for training and run your own fine tuned models
- You can have much higher generation speeds
- You can sell the GPU on the used market in ~2 years time for a significant portion of its value
- You can run other types of models like image, audio or video generation that are not available via an API, or cost significantly more
- Psychologically, you don’t feel like you have to constrain your token spending and you can, for instance, just leave an agent to run for hours or overnight without feeling bad that you just “wasted” $20
- You won’t be running the GPU at max power constantly
Or censorship avoidance
This is simply not true. Your heuristic is broken.
The recent Gemma 3 models, which are produced by Google (a little startup - heard of em?) outperform the last several OpenAI releases.
Closed does not necessarily mean better. Plus the local ones can be finetuned to whatever use case you may have, won't have any inputs blocked by censorship functionality, and you can optimize them by distilling to whatever spec you need.
Anyway all that is extraneous detail - the important thing is to decouple "open" and "small" from "worse" in your mind. The most recent Gemma 3 model specifically is incredible, and it makes sense, given that Google has access to many times more data than OpenAI for training (something like a factor of 10 at least). Which is of course a very straightforward idea to wrap your head around, Google was scrapign the internet for decades before OpenAI even entered the scene.
So just because their Gemma model is released in an open-source (open weights) way, doesn't mean it should be discounted. There's no magic voodoo happening behind the scenes at OpenAI or Anthropic; the models are essentially of the same type. But Google releases theirs to undercut the profitability of their competitors.
This one? https://artificialanalysis.ai/models/gemma-3-27b