saricles / Qwen3-Coder-Next-NVFP4-GB10

Qwen3-Coder-Next-NVFP4-GB10

NVFP4 quantization of Qwen/Qwen3-Coder-Next for NVIDIA DGX Spark (GB10).

Qwen3-Coder-Next is a 79.7B-parameter MoE coding model (512 experts, 10 active per token) with hybrid DeltaNet+attention architecture. This quantization uses a GB10-tuned ignore list that quantizes more aggressively than standard NVFP4 configurations.

Model Details

Quantization Details

• Method: Post-training quantization via LLM Compressor
• Calibration Dataset: HuggingFaceH4/ultrachat_200k (train_sft split)
• Calibration Samples: 64
• Max Sequence Length: 2048 tokens
• Environment: LLMCOMPRESSOR_MOE_CALIBRATE_ALL_EXPERTS=1

Ignore List (layers kept in BF16)

lm_head
model.embed_tokens
re:.*linear_attn.conv1d
re:.*linear_attn.in_proj_ba
re:.*mlp.gate$
re:.*mlp.shared_expert_gate$

Everything else — including in_proj_qkvz — is quantized to FP4. On GB10's 221 GB/s bandwidth, the bandwidth savings from quantizing these layers outweigh the FP4 kernel dispatch overhead.

Performance (Single NVIDIA DGX Spark — GB10, 128 GB)

Benchmarked with llama-benchy v0.3.3, 3 runs per config.

The hybrid DeltaNet+attention architecture means decode speed is constant regardless of context length — DeltaNet layers don't use KV cache.

Running on a Single DGX Spark

Docker image: avarok/dgx-vllm-nvfp4-kernel:v23 (vLLM 0.16.0-rc2, CUDA 13.0, SM 12.1)

Download the model:

huggingface-cli download saricles/Qwen3-Coder-Next-NVFP4-GB10 \
  --local-dir /opt/huggingface/models/Qwen3-Coder-Next-NVFP4-GB10

Launch:

docker run -d --name coder-next --gpus all --ipc=host --shm-size 32g \
  -v /opt/huggingface/models/Qwen3-Coder-Next-NVFP4-GB10:/models/Qwen3-Coder-Next-NVFP4-GB10 \
  -p 8000:8000 \
  -e VLLM_NVFP4_GEMM_BACKEND=marlin \
  -e VLLM_TEST_FORCE_FP8_MARLIN=1 \
  -e VLLM_USE_FLASHINFER_MOE_FP4=0 \
  -e VLLM_MARLIN_USE_ATOMIC_ADD=1 \
  -e MODEL=/models/Qwen3-Coder-Next-NVFP4-GB10 \
  -e PORT=8000 \
  -e MAX_MODEL_LEN=262144 \
  -e GPU_MEMORY_UTIL=0.90 \
  -e "VLLM_EXTRA_ARGS=--kv-cache-dtype fp8 --attention-backend flashinfer --enable-prefix-caching --enable-chunked-prefill --max-num-batched-tokens 8192 --max-num-seqs 64 --enable-auto-tool-choice --tool-call-parser qwen3_coder" \
  avarok/dgx-vllm-nvfp4-kernel:v23

Test it:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen3-Coder-Next-NVFP4-GB10",
    "messages": [{"role": "user", "content": "Write a Python function to find the longest common subsequence"}],
    "temperature": 0.7,
    "max_tokens": 2048
  }'

Notes

• At 42.7 GiB model weight + 0.90 GPU util, you get ~62 GiB for KV cache — enough for 5 concurrent 262K sessions.
• gpu_memory_utilization=0.93 works but leaves very little system headroom. 0.90 is safer.
• Decode speed is constant across context lengths thanks to the DeltaNet hybrid architecture.
• Marlin backend is 15% faster than VLLM_CUTLASS for this model's 512 experts.

Target Hardware

Quantized and tested on NVIDIA DGX Spark (GB10, 128 GB unified memory, 221 GB/s bandwidth). Should work on other Blackwell GPUs with NVFP4 support.

Acknowledgments

• Base model by Qwen
• Quantization tooling by vLLM / LLM Compressor