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Trinity large: An open 400B sparse MoE model

arcee.ai

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Re: Trinity large: An open 400B sparse MoE model

#21

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

Yep. You be right. Looks like they increased it earlier this month. Bummer!

Re: Trinity large: An open 400B sparse MoE model

#22

Earlier 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

So it's a rip off of a rip off, is that whats interesting?

Re: Trinity large: An open 400B sparse MoE model

#24

What 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?

```During development of the RSDB, we noted significant enough performance gains from it that we decided to integrate it during phase 3 of the Trinity Large training run instead of waiting for a later training run. While the data distributions between phase 2 and phase 3 make direct comparison difficult, the overall effect was notable: BatchHet reduced by a factor of 4.23x, and step-to-step variance reduced by a factor of 2.4x (see Figure 1), a significant improvement when compared to the default packing strategy. We note that training runs without the RSDB exhibit much higher values in the higher-order moments of the running loss distribution, which we believe to correlate with network instability during training. ```

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

#28
post #20

Earlier 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…

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 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

#29
post #20

Earlier 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…

Frankly, this reads as a lot of words that amount to an excuse for using only LMArena, and the rationale is quite clear: it’s for an unrelated argument that isn’t going to ring true to people, especially an audience of programmers who just spent the last year watching the AI go from being able to make coherent file edits to multi hour work.

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.

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