Step-3.5-Flash-NVFP4

Step-3.5-Flash-NVFP4

NVFP4-quantized version of stepfun-ai/Step-3.5-Flash, an open-source frontier-level reasoning model by StepFun with 196.81B total parameters and ~11B active parameters per token.

Note: HuggingFace displays ~111B params because it counts packed FP4 tensor elements (2 weights per byte). The actual model has 196.81B parameters, identical to the base model.

Model Description

Step 3.5 Flash is an open-source foundation model designed for frontier-level reasoning and agentic capabilities with exceptional efficiency. Key highlights from the base model:

• AIME 2025: 97.3%
• SWE-bench Verified: 74.4%
• LiveCodeBench-V6: 86.4%
• Terminal-Bench 2.0: 51.0%
• GAIA (no file): 84.5

This NVFP4 quantization reduces the model size from ~372 GB (BF16) to ~105 GB while preserving quality, making it practical to deploy on just 2 GPUs.

Quantization Details

During calibration, all 288 experts per MoE layer were activated to ensure every expert received calibration data, using a custom Step3p5MoEMLP calibration module.

Architecture

Layers 43-44 use a swiglustep activation (clipped SwiGLU with limit=7.0) on their MoE experts. All other MoE layers use standard SiLU. This requires vLLM support for swiglustep in the NVFP4 MoE kernels.

Requirements

This model requires vLLM with swiglustep MoE activation support. This is available in the following PR:

vllm-project/vllm#34478 -- Add swiglustep activation support for NVFP4 MoE backends

Until the PR is merged, install vLLM from the PR branch or from source with the changes applied.

Usage with vLLM

Serving

vllm serve tacos4me/Step-3.5-Flash-NVFP4 \
  --served-model-name step3p5-flash \
  --tensor-parallel-size 2 \
  --trust-remote-code \
  --reasoning-parser step3p5 \
  --enable-auto-tool-choice \
  --tool-call-parser step3p5 \
  --disable-cascade-attn

Offline Inference

from vllm import LLM, SamplingParams

llm = LLM(
    model="tacos4me/Step-3.5-Flash-NVFP4",
    tensor_parallel_size=2,
    trust_remote_code=True,
    max_model_len=4096,
    gpu_memory_utilization=0.95,
)

output = llm.generate(
    "Explain the significance of the number 42.",
    SamplingParams(max_tokens=256),
)
print(output[0].outputs[0].text)

Performance

Known Issues

1. FlashInfer MoE backend on Blackwell: The FlashInfer CUTLASS MoE backend may crash with illegal memory access on Blackwell GPUs (sm_120). Set VLLM_USE_FLASHINFER_MOE_FP4=0 as a workaround.

2. MTP weights not included: Speculative decoding (Multi-Token Prediction) weights from the base model are not included in this quantized checkpoint.

3. Minimum 2 GPUs required: The model requires ~105 GB, so it does not fit on a single 80/96 GB GPU. Use --tensor-parallel-size 2 or higher.

Acknowledgments

• Based on stepfun-ai/Step-3.5-Flash by StepFun
• Quantized with llm-compressor by the vLLM project
• NVFP4 MoE swiglustep activation support contributed to vLLM

Citation

If you use this model, please cite the original Step 3.5 Flash paper:

@misc{huang2026step35flashopen,
  title={Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters},
  author={Huang, Ailin and Li, Ang and others},
  year={2026},
  eprint={2602.10604},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2602.10604}
}

License

This model is released under the Apache 2.0 License, same as the base model.