What GPU do I need to run arcee-ai/Trinity-Large-Preview?
A 399B (MoE) language model for chat and instruction-following. 398.6B parameters, published in BF16. View on Hugging Face
Trinity-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.
What Trinity-Large-Preview is
Trinity-Large-Preview is a 399B-parameter mixture-of-experts language model published by Arcee AI on Hugging Face. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from Trinity-Large-Preview's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
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 | $6.88/hr |
| INT4 (quantized) | 185.6 GB | 222.8 GB | RTX A6000 | 5 | $1.81/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.
How to run Trinity-Large-Preview
Run Trinity-Large-Preview with vLLM
From arcee-ai/Trinity-Large-Preview's own deployment docs.
vllm serve arcee-ai/Trinity-Large-Preview \
--dtype bfloat16 \
--enable-auto-tool-choice \
--tool-call-parser hermesSource: https://huggingface.co/arcee-ai/Trinity-Large-Preview/raw/main/README.md
Run Trinity-Large-Preview with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Trinity-Large-Preview-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Trinity-Large-Preview-GGUFSource: https://huggingface.co/unsloth/Trinity-Large-Preview-GGUF
Deploy Trinity-Large-Preview on Aquanode
Aquanode has no one-click deploy template for Trinity-Large-Preview; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (891 GB VRAM or more).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Trinity models
- Trinity-Mini (26.1B, BF16)
- Trinity-Nano-Preview (6.1B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
