⚠️ Hardware
FP4 tensor cores exist only on Blackwell (B200 / GB200 / RTX 50xx). Serve there for the
speed/memory benefit. (Hopper/H200 lack FP4 kernels — this checkpoint is produced on H200 but
runs accelerated on Blackwell.)
Serving (vLLM on Blackwell)
vllm serve mkd-hossain/Keural-Nova-v1.2-experimental-NVFP4 \
--served-model-name Keural-Nova-v1.2 \
--tensor-parallel-size 2 \
--max-model-len 262144 \
--tool-call-parser qwen3_xml --enable-auto-tool-choice
- Non-thinking default (the fixed chat template ships here); tool calls are Qwen XML.
- Serve native 256K for agent/tool workloads (YaRN 1M only for genuine >256K requests).
- Multimodal (vision) works — the vision tower is kept high-precision.
Quantization details
- Tool:
llm-compressor (compressed-tensors), NVFP4 scheme.
- Calibration: 512 samples from the Keural Nova v1.2 SFT mix (Korean/English/code/tool-use), seq-len 2048.
- Coverage: 31,070 linear layers quantized (incl. 30,880 MoE experts + attention + Gated-DeltaNet
linear-attention); vision blocks +
lm_head + MTP excluded (bf16).
- Group scheme: weights 4-bit float / activations 4-bit float, per-block FP8 scales (NVFP4).
Accuracy
NVFP4 typically costs ~1–2% vs bf16 — the same small delta as the public
nvidia/RedHatAI NVFP4 Qwen3.6-A3B checkpoints. Validate on your Blackwell against the
bf16 base (Keural-Nova-v1.2-experimental)
on your workload (tool-calling, Korean, code) before production.
License & attribution
Apache-2.0, inheriting the base Qwen/Qwen3.6-35B-A3B license. Quantization recipe after
RedHatAI/Qwen3.6-35B-A3B-NVFP4. "Keural Nova" is a model by MKD.