What GPU do I need to run arcee-ai/Trinity-Large-Preview?
398.6B parameters, published in BF16. View on Hugging Face Full specs & deploy guide
Set up Trinity-Large-PreviewTrinity-Large-Preview is published by arcee-ai on Hugging Face, with 563 downloads and 181 likes to date. It's a AfmoeForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 742.5 GB | 891.0 GB | No capable live offer found | – | – |
| FP8 (quantized) | 371.3 GB | 445.5 GB | RTX PRO 6000 | 5 | $8.20/hr |
| INT4 (quantized) | 185.6 GB | 222.8 GB | RTX A6000 | 5 | $1.65/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Trinity-Large-Preview: common questions
Can Trinity-Large-Preview run on a single GPU?
Not on a desktop card. At BF16 it needs 891.0 GB of VRAM, more than a single 32 GB desktop card holds. No card currently listed on the marketplace both supports BF16 and has enough VRAM for it, so how many it would take is not something this page can answer today.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More arcee-ai models
- Trinity-Mini (26.1B, BF16)
- Trinity-Nano-Preview (6.1B, BF16)
- AFM-4.5B (4.6B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
