- Looks like a less good version of qwen 30b3a which makes sense bc it is slightly smaller. If they can keep that effiency going into the large one it'll be sick.
Trinity Large [will be] a 420B parameter model with 13B active parameters. Just perfect for a large Ram pool @ q4.
- Interesting. Always glad to see more open weight models.
I do appreciate that they openly acknowledge the areas where they followed DeepSeek's research. I wouldn't consider that a given for a US company.
Anyone tried these as a coding model yet?
by davidsainez
0 subcomment
- Excited to put this through its paces. It seems most directly comparable to GPT-OSS-20B. Comparing their numbers on the Together API: Trinity Mini is slightly less expensive ($0.045/$0.15 v $0.05/$0.20) and seems to have better latency and throughput numbers.
- Trinity Nano Preview: 6B parameter MoE (1B active, ~800M non-embedding), 56 layers, 128 experts with 8 active per token
Trinity Mini: 26B parameter MoE (3B active), fully post-trained reasoning model
They did pretraining on their own and are still training the large version on 2048 B300 GPUs
- > Trinity Large is currently training on 2048 B300 GPUs and will arrive in January 2026.
How long does the training take?
- Moe ≠ MoE
- A moe model you say? How kawaii is it? uwu
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