Qwen3.6-35B-A3B NVFP4 GGUF

NVFP4 GGUF quantizations of Qwen/Qwen3.6-35B-A3B, produced for use with llama.cpp.

This is a MoE model — 35B total parameters, 3B activated per token (8 of 256 experts). The expert FFN tensors are quantized to NVFP4 (NVIDIA's 4-bit float with E4M3 block scale), repacked from mmangkad/Qwen3.6-35B-A3B-NVFP4 (NVIDIA ModelOpt v0.43 calibration). Because the experts dominate the model's memory footprint, NVFP4-quantizing them gives most of the size reduction; the remaining tensors (attention, shared experts, SSM linear_attn blocks, embeddings) use a conventional GGUF quant.

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Why NVFP4? On NVIDIA Blackwell GPUs (RTX 50-series, B100/B200), llama.cpp uses native NVFP4 tensor-core MMA kernels (added in llama.cpp #22196) for the expert matmul — the dominant compute cost during MoE inference. On older GPUs the path falls back to dp4a/MMQ kernels, where these GGUFs run but offer no perf advantage over standard K-quants.

Files

Performance

Measured on an NVIDIA RTX 5090 (32 GB, Blackwell, sm_120), llama.cpp build c84e6d6db.

Variant comparison (single-stream, llama-bench 512 in / 64 out)

Counterintuitively the smallest variant is the fastest here — for an MoE model only 3B parameters are active per token, so memory bandwidth dominates and the tighter quant wins. Pick Q4_K_M unless you specifically need higher precision for the attention/embedding tensors.

Note on MoE expert kernels (honest comparison vs stock Q4_K_M)

For our two dense NVFP4 GGUFs (Qwen3.6-27B and Gemma-4-31B-IT), our NVFP4-Q4_K_M variant beats stock Q4_K_M on serving throughput by ~5–14% on RTX 5090.

For this MoE model, however, llama.cpp's Q4_K_M MMQ kernel currently outperforms the NVFP4 expert path. At parallel=8, batched serving:

The NVFP4 MoE kernel has room to optimize upstream — we'll refresh these GGUFs (no re-conversion needed, just re-bench) once that lands. Until then this release is most useful for: (a) format parity with vLLM/SGLang/TensorRT-LLM checkpoints, (b) calibrated NVFP4 quality vs RTN, and (c) running the model bit-for-bit identically to the upstream NVIDIA-style quant.

Usage

Text-only (CLI)

llama-cli -m Qwen3.6-35B-A3B-NVFP4-Q4_K_M.gguf -ngl 999 -c 8192 -p "Your prompt here"

Multimodal (server, vision + text)

llama-server \
  -m Qwen3.6-35B-A3B-NVFP4-Q4_K_M.gguf \
  --mmproj mmproj-Qwen3.6-35B-A3B-F16.gguf \
  -ngl 999 -c 32768 \
  --host 0.0.0.0 --port 8080

Then POST to /v1/chat/completions with image content blocks — see the llama.cpp multimodal docs.

Recommended sampler

Qwen3.6 is a thinking model. Default chat template enables <think> blocks. For non-thinking usage pass --reasoning off (in llama-cli) or set chat_template_kwargs.enable_thinking=false in the API.

About the architecture

Qwen3.6-35B-A3B is a hybrid attention + SSM MoE model with 40 layers, 256 experts (8 active per token), and 35B total / 3B active parameters. The NVFP4 source from mmangkad keeps the standard attention projections, shared expert FFN (*_shexp), SSM linear_attn blocks, and embeddings at higher precision — only the routed expert FFN matmul (120 tensors: 40 layers × 3 projections) is NVFP4. The variants above differ only in how those non-expert tensors are stored.

Sources & credits

• Base model: Qwen/Qwen3.6-35B-A3B by Alibaba Qwen team — Apache 2.0
• NVFP4 calibration source: mmangkad/Qwen3.6-35B-A3B-NVFP4 (NVIDIA ModelOpt v0.43)
• mmproj source: official BF16 weights from Qwen/Qwen3.6-35B-A3B
• Tooling: llama.cpp convert_hf_to_gguf.py and llama-quantize

License

Apache 2.0, inherited from the upstream model.