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Atlas3D
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Model Details
Model Provider
Atlas3D
Model Tree
autotrust/JEV-27B-VL
Input Modalities
Output Modalities
Supported Functionality
/v1/decide)Same harness and states as the bf16 model, compared one decision at a time:
| NVFP4 (this) | FP8 | bf16 | |
|---|---|---|---|
| Top choice agrees with bf16 | 90.5% (266/294) | 97.1% | reference |
| Largest probability change on any option | 0.164 | 0.061 | none |
| Sanity scenarios | 6/6 | 6/6 | 6/6 |
| VRAM in use, RTX PRO 6000 | 37.6 GB | 42.6 GB | about 78 GB |
| 24-decision tick, p50 | 1130 ms | 1152 ms | 1253 ms |
This is a narrow test of the decision head. System 2 (free-text generation) quality was not separately benchmarked.
Serve it like the original (vLLM with --quantization compressed-tensors). serve_decide.py is
included. FlashInfer compiles its FP4 kernels on first use, so set CUDA_HOME to your CUDA toolkit.
Apache-2.0, as with the original. This is a modified (quantized) version of autotrust/JEV-27B-VL,
which is itself based on Qwen/Qwen3.8-27B. LICENSE is included unchanged.