Gemma-4-31B-IT-NVFP4

NVFP4 4-bit quantization reduces GPQA Diamond performance by just 0.25 percentage points (84.3% to 84.05%) while enabling deployment on edge devices with 75% memory reduction. The quantized model maintains competitive performance against larger models, scoring within 1.5 points of Qwen 3.5 27B on GPQA Diamond (84.3% vs 85.8%) and outperforming the median of similar-size open models by 24 points on Artificial Analysis Intelligence Index v4.0 (39 vs 15). NVIDIA's implementation preserves critical weights through mixed-precision strategies, keeping attention heads at higher precision while aggressively quantizing feedforward layers.

The model achieves its 2150 Codeforces ELO through three key innovations: hybrid attention with a 5:1 ratio of local sliding-window to global attention layers reducing computational complexity while preserving long-range dependencies, Per-layer Embeddings (PLE) architecture that adapts representations dynamically through training, and Proportional RoPE (p-RoPE) maintaining positional encoding accuracy across the full 256K context window. This architecture enables 80% accuracy on LiveCodeBench v6 (50.9 points above Gemma 3 27B's 29.1%) and native function calling support with structured JSON output generation for complex programming tasks.

At $0.14 per million input tokens and $0.40 per million output tokens, Gemma-4-31B-IT-NVFP4 costs 71-86% less than GPT-4o ($5/$15) and 93-97% less than Claude Opus ($15/$75) while delivering 85.2% MMLU Pro accuracy. For a typical workload processing 100M input tokens and generating 25M output tokens monthly, costs would be $24 versus $1,375 for GPT-4o or $3,375 for Claude Opus. The NVFP4 quantization further reduces deployment costs by enabling single-GPU inference on hardware supporting 8-16GB VRAM versus the 64-128GB typically required for 30B+ parameter models.

Video processing is limited to 60 seconds at 1fps (60 frames maximum), making it unsuitable for real-time video analysis or longer content requiring costs of 70-1120 tokens per frame depending on resolution settings. Image understanding scores 76.9% on MMMU Pro Vision (27.2 points above Gemma 3), but falls short of specialized vision models like GPT-4V (88.4%) or Claude 3.5 Vision (91.2%). The model processes images with configurable token budgets from 70 (low-res thumbnails) to 1120 (high-detail analysis), requiring careful optimization between accuracy and inference cost for vision-heavy workloads.