Earlier quoted context omitted.
Talking about RAM prices, you can still get a framework Max+ 395 with 128GB RAM for ~$2,459 USD. They have not increased the price for it yet. https://frame.work/products/desktop-diy-amd-aimax300/configu...
Pretty sure those use to be $1999 ... but not entirely sure
Trinity large: An open 400B sparse MoE model
21–30 of 83 posts
Re: Trinity large: An open 400B sparse MoE model
#22Earlier quoted context omitted.
Well, it is, it's your data to begin with after all but admitting that would create some problems.
This model is sort of interesting since it seems to be using a lot of synthetic training data – but your point stands
Re: Trinity large: An open 400B sparse MoE model
#23Re: Trinity large: An open 400B sparse MoE model
#24What did they do to make the loss drop so much in phase 3? Also, why are they comparing with Llama 4 Maverick? Wasn’t it a flop?
Page 9 of the technical report has more details, but it looks like they found some data prep methods as well as some other optimizations that overall worked out really well. I don't think it was any one particular thing.
As far as Llama 4 goes, it was only referenced as a similarly sized model, they called it one of their model "peers"; I don't think they intended any sort of quality comparison. Llama 4 was notable for sparsity, despite its poor performance and reception, some of the things they achieved technically were solid, useful research.
Re: Trinity large: An open 400B sparse MoE model
#25How do they plan to monetize?
Re: Trinity large: An open 400B sparse MoE model
#26Is anyone excited to do ablative testing on it?
Re: Trinity large: An open 400B sparse MoE model
#27The only thing I question is the use of Maverick in their comparison charts. That's like comparing a pile of rocks to an LLM.
Re: Trinity large: An open 400B sparse MoE model
#28Earlier quoted context omitted.
The price/scaling of training another same class model always seems to be dropping through the floor but training models which score much better seems to be hitting a brick wall. E.g. gemini-3-pro tops the lmarena text chart today at 1488 vs 1346 for gpt-4o-2024-05-13. That's a win rate of 70% (where 50% is equal chance of winning) over 1.5 years. Meanwhile, even the open weights stuff OpenAI gave away last summer sc…
> E.g. gemini-3-pro tops the lmarena text chart today at 1488 vs 1346 for gpt-4o-2024-05-13. That's a win rate of 70% (where 50% is equal chance of winning) over 1.5 years. Meanwhile, even the open weights stuff OpenAI gave away last summer scores between the two. Why do you care about LM Arena? It has so many problems, and the fact that no one would suggest using GPT-4o for doing math or coding right now, or much of…
The particular benchmark in the example is fungible but you have to pick something to make a representative example. No matter which you pick someone always has a reason "oh, it's not THAT benchmark you should look at". The benchmarks from the charts in the post exhibit the same as described above.
If someone was making new LLMs which were consistently solving Erdos problems at rapidly increasing rates then they'd be showing how it does that rather than showing how it scores the same or slightly better on benchmarks. Instead the progress is more like years since we were surprised LLMs were writing poetry to massage out an answer to one once. Maybe by the end of the year a few. The progress has definitely become very linear and relatively flat compared to roughly the initial 4o release. I'm just hoping that's a temporary thing rather than a sign it'll get even flatter.
Re: Trinity large: An open 400B sparse MoE model
#29Earlier quoted context omitted.
> E.g. gemini-3-pro tops the lmarena text chart today at 1488 vs 1346 for gpt-4o-2024-05-13. That's a win rate of 70% (where 50% is equal chance of winning) over 1.5 years. Meanwhile, even the open weights stuff OpenAI gave away last summer scores between the two. Why do you care about LM Arena? It has so many problems, and the fact that no one would suggest using GPT-4o for doing math or coding right now, or much of…
It'd certainly be odd if people were recommending old LLMs which score worse, even if marginally. That said, 4o is really a lot more usable than you're making it out to be. The particular benchmark in the example is fungible but you have to pick something to make a representative example. No matter which you pick someone always has a reason "oh, it's not THAT benchmark you should look at". The benchmarks from the cha…
LMArena is, de facto, a sycophancy and Markdown usage detector.
Two others you can trust, off the top of my head, are LiveBench.ai and Artifical Analysis. Or even Humanity’s Last Exam results. (Though, frankly, I’m a bit suspicious of them. Can’t put my finger on why. Just was a rather rapid hill climb for a private benchmark over the last year.)
FWIW GPT 5.2 unofficial marketing includes the Erdos thing you say isn’t happening.