gemma-4-31B-it-speculator.eagle3-NVFP4

gemma-4-31B-it-speculator.eagle3-NVFP4 (NVFP4)

NVFP4 quantization of RedHatAI/gemma-4-31B-it-speculator.eagle3 — RedHat / Neural Magic's official EAGLE-3 speculator drafter for Gemma 4 31B (dense).

What this is

Drop-in replacement for the BF16 drafter, 3× smaller (4.5 GB → ~1.5 GB) and ~1.5× faster per draft step on Blackwell with native FP4 tensor cores. Targets the same verifier model as the BF16 source.

Use it with vLLM

vllm serve RedHatAI/gemma-4-31B-it-NVFP4 \
  --tensor-parallel-size 1 \
  --speculative-config '{
    "model": "AEON-7/gemma-4-31B-it-speculator.eagle3-NVFP4",
    "num_speculative_tokens": 3,
    "method": "eagle3"
  }' \
  --max-num-seqs 8 \
  --kv-cache-dtype fp8 \
  --enable-chunked-prefill \
  --enable-prefix-caching

Verifier can be any Gemma 4 31B (dense) variant — base, instruct, NVFP4, or fine-tunes (abliterated, domain-tuned, etc.). EAGLE drafters are architecture-bound, not weights-bound; output distribution is provably the verifier's.

Quantization recipe

Performance expectations

Acceptance lengths (relative to BF16 source — RedHat's published numbers):

Roughly 8-12% acceptance loss vs BF16, more than offset by per-step speedup on Blackwell native FP4 hardware.

Spark / DGX Spark deployment

Tested on NVIDIA DGX Spark (GB10, sm 12.1, 128 GB unified memory) using the ghcr.io/aeon-7/vllm-spark-gemma4-nvfp4:latest image (eugr nightly with sm_120-compiled FlashInfer CUTLASS + VLLM_CUTLASS NVFP4 kernels).

Single-stream wall-clock: 2.0-2.5× speedup over no spec decode on chat workloads.

Files

• model.safetensors — NVFP4 quantized drafter weights (~1.5 GB)
• config.json — Eagle3 speculator config (carries verifier reference)
• config.py — Custom Eagle3SpeculatorConfig class (custom_code, required for trust_remote_code=True)
• tokenizer.json, tokenizer_config.json — Verifier tokenizer (Gemma 4)
• hf_quant_config.json — ModelOpt NVFP4 quantization metadata
• modelopt_state.pt — Full modelopt state for re-export

License

Apache 2.0 (matches base model). NVFP4 quantization is a derivative work contributed under the same terms.

Provenance

Created by quantizing RedHatAI/gemma-4-31B-it-speculator.eagle3 with NVFP4_DEFAULT_CFG calibrated against RedHatAI/gemma-4-31B-it-NVFP4 outputs. Methodology adapted from RedHat AI's published Gemma 4 NVFP4 target recipe + standard EAGLE-3 calibration practice.

See also

• BF16 source: RedHatAI/gemma-4-31B-it-speculator.eagle3
• Verifier (NVFP4): RedHatAI/gemma-4-31B-it-NVFP4
• EAGLE-3 paper: arXiv:2503.01840
• Speculators library: vllm-project/speculators

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