gemma-4-31B-it-NVFP4

HivenetQuant/gemma-4-31B-it-NVFP4

NVFP4 (nvfp4) W4A4 quantization of google/gemma-4-31B-it — FP4 (E2M1) weights and activations with FP8 block scales. The MLP is the 4-bit tier; the attention layers the search protects are kept at FP8. The per-layer split is chosen automatically by a weight-sensitivity search and applied with GPTQ.

The checkpoint targets NVIDIA Blackwell's native FP4 tensor cores (e.g. RTX 5090); the benchmark below compares it against the original FP16 weights across the evaluated tasks.

Preserved (kept in BF16, not quantized): the vision encoder (model.visual*), plus lm_head and norm/router layers. So multimodal understanding is fully retained — see the vision (MMMU-Pro / AI2D) row(s) in the benchmark below.

Serving with vLLM

Runs on NVIDIA Blackwell (e.g. RTX 5090) with native NVFP4. This is the exact configuration the benchmarks below were validated on (FP8 KV cache, 128k context):

vllm serve HivenetQuant/gemma-4-31B-it-NVFP4 \
    --tensor-parallel-size 2 \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.90 \
    --kv-cache-dtype fp8 \
    --reasoning-parser gemma4 \
    --enable-auto-tool-choice \
    --tool-call-parser gemma4 \
    --chat-template /chat_template.jinja

All numbers below were produced with this serve config — NVFP4 weights, FP8 KV cache, 128k context.

Quantization recipe

• Method: post-training quantization with llm-compressor → compressed-tensors. The per-layer NVFP4-vs-FP8 split is chosen automatically by a weight-sensitivity search (ModelOpt supernet probe + Kneedle elbow), then the NVFP4 weights are recovered with GPTQ block-wise reconstruction. The exact per-layer tiers are in recipe_summary.json.
• Precision (quantized tier): W4A4 — FP4 (E2M1) weights and activations with FP8 per-block scales, group size 16. 54 attention layers are kept at FP8 (the protection tier); 222 modules stay BF16 (lm_head/norms, the vision tower).
• Protection axis: this is the FP8-protected release — the smaller, faster of the two (attention at FP8).
• Baseline = FP16, not FP8. Every Δ in the benchmark below is measured against the original full-precision weights — a stricter reference than cards that quantize from, and compare against, an already-lossy FP8 checkpoint.

Scope of this evaluation

This card reports a paired FP16 ↔ NVFP4 comparison — identical prompts, harness and decoding run head-to-head on both precisions — across a diverse task set. The goal is to show that NVFP4 quantization introduces no meaningful degradation versus the original weights, not to re-establish the base model's absolute capability.

A paired delta is detectable at far smaller sample sizes than an absolute score, so we evaluate a representative sample per task (exact sizes and decoding settings are in REPRODUCIBILITY.md) rather than every full benchmark set — a statistically sound degradation check that avoids the considerable compute and energy of re-running suites the base model has already published.

For absolute capability and full-suite results, see the base model google/gemma-4-31B-it and independent leaderboards: NVFP4 tracks FP16 within noise on every dimension tested here, so those numbers carry over.

Benchmark vs FP16

Speed

Measured with vLLM bench serve (synthetic random prompts, --ignore-eos) — vllm bench serve. Input lengths 1,024, 10,000 tokens, 512 output tokens, concurrency 1, 10, at TP4. Served with FP8 KV cache, max-model-len 131,072, gpu-mem 0.9, on NVIDIA GeForce RTX 5090, vllm/vllm-openai:v0.23.0. FP16 is measured identically at the same TP, so each ratio isolates the quantization effect.

decode = single-stream (batch 1) output rate; prefill = prompt ingestion (input tokens ÷ time-to-first-token); throughput = aggregate output rate under concurrent load.

Reproducing these numbers — the serving config, pinned harness versions, and per-dataset sampling / seeds / decisions contract are in REPRODUCIBILITY.md (standard open-source tools, no proprietary harness).

About

Published by HivenetQuant, the Hugging Face account for Hivenet's model-optimization work. Hivenet is Antimatter's cloud and AI-inference platform. Antimatter brings together energy and power infrastructure through Antimatter US, modular data centers through Policloud, and cloud software and AI inference through Hivenet.

This checkpoint was quantized and benchmarked by Hivenet's AI Products team on Antimatter's Policloud infrastructure. We publish the precision plan, benchmark results, serving configuration, and reproducibility details so developers can examine the trade-offs and run the model on compatible NVIDIA Blackwell hardware. Our broader aim is to make high-performance inference easier to deploy on infrastructure that operators can choose and control.