Property	Value
Base Model	Qwen/Qwen3-Coder-Next
Architecture	Qwen3NextForCausalLM (Hybrid DeltaNet + Attention + MoE)
Parameters	80B total, 3B activated per token
Experts	512 total, 10 activated + 1 shared
Layers	48
Context Length	262,144 tokens (256K)
Quantization	NVFP4 (FP4 weights + FP4 activations)
Size	45GB (down from ~149GB BF16, 70% reduction)
Format	compressed-tensors

Quantization Details

Quantized using llmcompressor 0.9.0.1.

python

NUM_CALIBRATION_SAMPLES = 20
MAX_SEQUENCE_LENGTH = 2048
DATASET = "HuggingFaceH4/ultrachat_200k" (train_sft)
moe_calibrate_all_experts = True

# Layers kept in BF16
ignore = [
    "lm_head",
    "re:.*mlp.gate$",               # MoE router gates
    "re:.*mlp.shared_expert_gate$", # Shared expert gates
    "re:.*linear_attn.*",           # DeltaNet linear attention
]

Benchmark Results

MMLU-Pro

Model	Accuracy	Delta
BF16	52.90%	-
NVFP4	51.27%	-1.63%

Context Length Testing

Successfully tested up to 128K tokens with FP8 KV cache (Not enough VRAM to test any higher context).

Usage with vLLM

Requires vLLM with NVFP4 support (0.16.0+), Transformers 5.0.0+

bash

#vllm Serving
vllm serve GadflyII/Qwen3-Coder-Next-NVFP4 \
    --tensor-parallel-size 2 \
    --max-model-len 131072 \
    --kv-cache-dtype fp8

License

Apache 2.0 (same as base model)

Acknowledgments

Qwen Team for the base model
RedHatAI for the quantization approach reference
vLLM Project for llmcompressor