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dudeman2512
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
dudeman2512
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
| Variant | Format | Size | vs BF16 | Mean rel. error | Linears quantized | Left BF16 |
|---|---|---|---|---|---|---|
| Qwen3.8-27B-FP8 | float-quantized | 30.35 GB | 55% | 0.0264 | 615 | 0 |
| Qwen3.8-27B-NVFP4 ← this one | nvfp4-pack-quantized | 19.29 GB | 35% | 0.0950 | 615 | 0 |
| Qwen3.8-27B-int4 | pack-quantized | 18.31 GB | 33% | 0.1178 | 588 | 27 |
Mean relative error is ||dequant(W) - W|| / ||W||, averaged over a sample of quantized Linear layers, measured against the original BF16 weights. Lower is better.
| Format | nvfp4-pack-quantized |
| Weight bits | 4 |
| Group size | 16 |
| Strategy | tensor_group |
| Linears quantized | 615 |
| Left in BF16 | 0 |
| Shards | 5 |
| On disk | 19.29 GB |
bash
vllm serve dudeman2512/Qwen3.8-27B-NVFP4
Every produced tensor is checked for shape/dtype conformance against what the server expects, then reconstruction error is measured against the source BF16 weights, before anything is published. The numbers in the table above are those measurements — not estimates.
| Mean relative error | 0.0950 |
| Shape/dtype conformance failures | 0 |