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Sponsored by
APIMart
arcee-ai / Trinity-Mini-NVFP4
Model's Last Updated: May 29 2026
text-generation
Introduction of Trinity-Mini-NVFP4
Model Details of Trinity-Mini-NVFP4
Trinity Mini NVFP4
This repository contains the NVFP4 quantized weights of Trinity-Mini for deployment on NVIDIA Blackwell GPUs.
Trinity Mini is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
This model is tuned for reasoning, but in testing, it uses a similar total token count to competitive instruction-tuned models.
Trinity Mini is trained on 10T tokens gathered and curated through a key partnership with Datology , building upon the excellent dataset we used on AFM-4.5B with additional math and code.
Training was performed on a cluster of 512 H200 GPUs powered by Prime Intellect using HSDP parallelism.
More details, including key architecture decisions, can be found on our blog here
Model Details
- Model Architecture: AfmoeForCausalLM
- Parameters: 26B, 3B active
- Experts: 128 total, 8 active, 1 shared
- Context length: 128k
- Training Tokens: 10T
- License: OpenMDW-1.1
-
Recommended settings:
- temperature: 0.15
- top_k: 50
- top_p: 0.75
- min_p: 0.06
Benchmarks
Quantization Details
-
Scheme:
NVFP4 (
nvfp4_mlp_only— MLP/expert weights only, attention remains BF16) - Tool: NVIDIA ModelOpt
- Calibration: 512 samples, seq_length=2048, all-expert calibration enabled
- KV cache: Not quantized
Running with vLLM
Requires vLLM >= 0.18.0. Native FP4 compute requires Blackwell GPUs; older GPUs fall back to Marlin weight decompression automatically.
Blackwell GPUs (B200/B300/GB300) — Docker (recommended)
docker run --runtime nvidia --gpus all -p 8000:8000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
vllm/vllm-openai:v0.18.0-cu130 \
arcee-ai/Trinity-Mini-NVFP4 \
--trust-remote-code \
--gpu-memory-utilization 0.90 \
--max-model-len 8192
Hopper GPUs (H100/H200) and others
vllm serve arcee-ai/Trinity-Mini-NVFP4 \
--trust-remote-code \
--gpu-memory-utilization 0.90 \
--max-model-len 8192 \
--host 0.0.0.0 \
--port 8000
Note (Blackwell pip installs): If installing vLLM via pip on Blackwell rather than using Docker, native FP4 kernels may produce incorrect output due to package version mismatches. As a workaround, force the Marlin backend:
export VLLM_NVFP4_GEMM_BACKEND=marlin
vllm serve arcee-ai/Trinity-Mini-NVFP4 \
--trust-remote-code \
--moe-backend marlin \
--gpu-memory-utilization 0.90 \
--max-model-len 8192 \
--host 0.0.0.0 \
--port 8000
Marlin decompresses FP4 weights to BF16 for compute, providing the full memory compression benefit (~3.7× vs BF16) but not native FP4 compute speedup. On Hopper GPUs (H100/H200), Marlin is selected automatically and no extra flags are needed.
License
Trinity-Mini-NVFP4 is released under the OpenMDW-1.1 license.
Runs of arcee-ai Trinity-Mini-NVFP4 on huggingface.co
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More Information About Trinity-Mini-NVFP4 huggingface.co Model
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Updated:May 29 2026
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