Model overview
> [!TIP] > **There is a newer version of this model:** > gemma-4-31B-it-qat-NVFP4-mse-Blackwell. > Same base model and calibration set, but with MSE-based weight scaling > instead of absmax, which keeps outliers from crushing the rest of each block. > This repo stays as it is so existing deployments don't change under their feet. Gemma 4 31B IT quantized to NVFP4 for native FP4 inference on NVIDIA Blackwell GPUs. Quantized from Google's unquantized QAT weights (`google/gemma-4-31B-it-qat-q4_0-unquantized`) with NVIDIA Model Optimizer (`modelopt` 0.44.0), default absmax scaling. - **Base Model:** google/gemma-4-31B-it-qat-q4_0-unquantized - **Quantization Format:** `NVFP4` (native FP4 on Blackwell) - **Group Size:** 16 (per-group scaling) - **Excluded Modules:** `lm_head`, `model.embed_vision*`, `model.vision_tower*` (kept in high precision) - **KV cache scales:** none in the checkpoint; use `--kv-cache-dtype fp8` and let vLLM scale dynamically - **Producer:** NVIDIA Model Optimizer (`modelopt` 0.44.0)
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。