NVIDIA-Nemotron-3.5-Transcribe-30B-A4B-DFlash2

This is a DFlash2 draft model for speculative decoding with NVIDIA-Nemotron-3.5-Transcribe-30B-A4B. It speeds up generation by proposing several tokens at once, which the main model then verifies.

This is not a standalone model. It cannot transcribe audio on its own and must be served together with Nemotron-3.5-Transcribe-30B-A4B. For the model description, capabilities, benchmark results, prompts, training data, and the Model Card++ subcards, see the main model card.

This model is ready for commercial or non-commercial use.

Model Summary

Parameters	Approximately 570M
Architecture	DFlash2 block-diffusion draft model with 6 layers, conditioned on the hidden states of 6 layers of the main model (layers 1, 5, 19, 29, 41, and 51)
Precision	BF16
Draft Length	Up to 15 tokens per step; 7 is the recommended starting point
Training	Trained to predict the greedy outputs of the main model on examples from its supervised fine-tuning mixture
Compatible Main Models	Nemotron-3.5-Transcribe-30B-A4B in BF16, FP8, and NVFP4
License	OpenMDW-1.1

License / Terms of Use

Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Users are responsible for complying with this license and with any third-party licenses or notices applicable to components included in the distributed model materials.

Quick Start

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1. Set up vLLM 0.32.0 and the latest NeMo Speech main branch, and download the main model, as described in the main model's inference setup.

2. vLLM 0.32.0 needs a small fix to its DFlash2 implementation; without it, the server can fail when requests finish. Apply it once to the environment:

python - <<'EOF_PATCH'
from pathlib import Path
import vllm
path = Path(vllm.__file__).parent / "model_executor/models/qwen3_dflash2.py"
source = path.read_text()
if "safe_anchor_token_ids" not in source:
    old = "    successors = successor_table[candidate_ids]\n"
    anchor = "            anchor_token_ids[:, None, None].expand(-1, 1, top_k),"
    assert source.count(old) == 1 and source.count(anchor) == 1, "unexpected vLLM version"
    source = source.replace(old, (
        "    safe_anchor_token_ids = anchor_token_ids.clamp(\n"
        "        min=0, max=predecessor_table.shape[0] - 1\n"
        "    )\n" + old
    )).replace(anchor, "            safe_anchor_token_ids[:, None, None].expand(-1, 1, top_k),")
    path.write_text(source)
EOF_PATCH

The fix modifies vLLM source code (Copyright contributors to the vLLM project), which is licensed under the Apache License, Version 2.0, not under OpenMDW-1.1; see THIRD_PARTY_NOTICES.md.

3. Start the main model's server as described in its Quick Start, with these changes: replace the MTP --speculative-config with the DFlash2 configuration below, remove --middleware, and disable the CTC timestamp alignment. The draft model is downloaded from Hugging Face automatically.

--speculative-config '{"method":"dflash","num_speculative_tokens":7,"model":"nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B-DFlash2"}'

The CTC timestamp alignment is not available with DFlash2:

--hf-overrides '{"ctc_timestamps":{"enabled":false}}'

vLLM runs DFlash2 only on its V2 model runner, so also run unset VLLM_USE_V2_MODEL_RUNNER before starting the server.

Send requests exactly as to the main model; the draft model does not change the API.

Contributing

Contributions are welcome. All contributions must be signed off under the Developer Certificate of Origin; see CONTRIBUTING.md.

Limitations

• Requires the main model. Use this draft model only with Nemotron-3.5-Transcribe-30B-A4B. It was trained against the released checkpoint, and its speedup on other checkpoints has not been measured.
• Same output as the main model. The main model verifies every proposed token, so with greedy decoding the output matches that of the main model without speculative decoding, apart from small numerical differences. The draft model affects speed, not accuracy.
• No word timestamps. The DFlash2 integration in vLLM does not support the CTC timestamp alignment. Use MTP speculative decoding, or none, when word timestamps are needed.
• Speedup depends on the workload. The acceptance length is lower on long recordings than on short ones. On a single RTX PRO 6000 Blackwell GPU, DFlash2 gives the lowest latency for a single request, whereas MTP can give higher throughput on 10-minute recordings; see the tech report for measurements. DFlash2 and MTP are alternatives and are not combined.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards of the main model.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.