LLM
How to deploy Trinity-Large-Preview on a GPU cloud
A 399B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
Trinity-Large-Preview size and hardware requirements
398.6B
Total parameters
Mixture-of-experts: 4 of 256 experts active per token (exact active-parameter count not stated on the model card)
Active parameters
BF16
Published precision
891.0 GB
Min VRAM (native)
| Precision | Weight size on disk | 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 |
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 pod