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ThakiCloud
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Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
ThakiCloud
Model Tree
ThakiCloud/Qwen3.8-27B-Human-KO
Input Modalities
Output Modalities
Supported Functionality
| Axis | bf16 original | NVFP4 (this model) | Verdict |
|---|---|---|---|
| T=0 anchor response | short polite-register short answers | identical output | style survives |
| Free-generation bullet-list rate (n=200) | 2.0% | 2.0% | Same |
| Free-generation median length | 220 characters | 249 characters | Similar |
| KMMLU (same items, n=500) | — | Δ−2.4pp, z=1.09 | not distinguishable |
| HAE-RAE (same items, n=199) | — | Δ+4.9pp, z=0.41 | not distinguishable |
Measured serving (1x B200, vLLM 0.28, gpu-mem-util 0.90): KV cache 2,143,277 tokens, maximum concurrency 8.18x at a 262,144-token request size.
Honest caveats:
python
from vllm import LLMllm = LLM(model="ThakiCloud/Qwen3.8-27B-Human-KO-NVFP4",max_model_len=262144, trust_remote_code=True)
NVFP4 kernels run on SM100+ (B200, etc.). On architectures below that, the model may fail to load or run much slower on a fallback kernel. If you need the bf16 original, use the base_model repo.
Apache-2.0 (same as the original). NOTICE and DATA_PROVENANCE.md carry the same content as the original repo.