NVIDIA releases Gemma 4 31B quantized model with 256K context, multimodal capabilities

NVIDIA Quantizes Google DeepMind's Gemma 4 31B for Efficient Inference

NVIDIA has released an NVFP4-quantized version of Google DeepMind's Gemma 4 31B IT model on Hugging Face, designed to run inference on consumer-grade NVIDIA GPUs while maintaining frontier-level performance for reasoning, coding, and multimodal tasks.

Model Specifications

The base Gemma 4 31B IT contains 30.7B parameters and supports a 256K-token context window—enabling extended document processing and multi-turn conversations. The model is multimodal, accepting text, image, and video inputs (up to 60 seconds at 1 FPS). It supports configurable visual token budgets (70, 140, 280, 560, 1120 tokens) and variable image aspect ratios. Vocabulary size is 262,144 tokens.

The model covers over 140 languages and uses a hybrid attention mechanism combining local sliding-window and global attention with Proportional RoPE for long-context stability.

Quantization Impact

NVIDIA's NVFP4 quantization (performed with nvidia-modelopt v0.42.0) shows minimal performance degradation:

• GPQA Diamond: 75.71% → 75.46% (−0.25 points)
• AIME 2025: 66.25% → 65.94% (−0.31 points)
• MMLU Pro: 85.25% → 84.94% (−0.31 points)
• LiveCodeBench (pass@1): 70.90% → 70.63% (−0.27 points)
• Scicode (pass@1): 33.61% → 33.18% (−0.43 points)
• Terminal-Bench Hard: 27.08% → 27.08% (no change)

The quantized model reduces memory requirements and enables deployment on NVIDIA Hopper architecture (H100) and newer Blackwell systems via vLLM.

Training Data and Licensing

The underlying Gemma 4 model was trained on large-scale multimodal data (text, code, images, audio) with a knowledge cutoff of January 2025. Google DeepMind applied CSAM filtering and safety processing. NVIDIA calibrated the quantized version using the CNN DailyMail dataset (300K+ articles).

The model is available under Apache License 2.0 with NVIDIA's Open Model License Agreement governing usage. It is cleared for both commercial and non-commercial use, though NVIDIA notes the model may amplify biases and toxicity from its training data.

Known Limitations

Gemma 4 31B IT can generate inaccurate information, omit key details, and produce toxic responses—particularly when prompted with adversarial inputs. The model does not blur or maintain aspect ratios of people, personal health information, or copyrighted content in images.

NVIDIA recommends developers validate the model against internal requirements for specific industries and use cases before deployment.

Deployment and Integration

The quantized model is optimized for vLLM inference engine with recommended tensor parallelism of 8 on H100 hardware. NVIDIA reports 6,292 downloads on Hugging Face in the past month, though no commercial pricing has been announced for hosted inference.

What This Means

This release makes frontier-class multimodal reasoning accessible on consumer and datacenter GPUs without API dependencies. The minimal performance loss (<0.5% on most benchmarks) validates NVFP4 quantization for production deployments. However, the lack of hosted inference offerings means organizations must self-host—requiring GPU infrastructure and operational overhead. The 256K context and multimodal capabilities position Gemma 4 31B as a direct competitor to larger proprietary models for organizations prioritizing deployment flexibility and cost control.