Gemma 4 31B Quantization Comparison: Best FP8, NVFP4, and INT4 Models

2026-04-20 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

An evaluation of various quantized versions of the Gemma 4 31B large language model was conducted to assess their impact on accuracy, inference speed, token efficiency, and memory consumption. The study compared the original google/gemma-4-31B-it model (62.6 GB) against several quantized variants, including NVFP4, INT4, and FP8 versions from providers like RedHatAI, NVIDIA, cyankiwi, LilaRest, and Intel, with sizes ranging from 19.2 GB to 33.3 GB. All models were served using vLLM 0.19 on a B200 GPU for evaluation, with latency measured on an RTX Pro 6000. Key findings indicate that while quantization significantly reduces model size, variants with quantized attention layers, such as Intel/gemma-4-31B-it-int4-AutoRound and RedHatAI/gemma-4-31B-it-NVFP4, showed a slight degradation in accuracy, particularly on MMLU-Pro. Token efficiency remained largely stable across most quantized models, with Intel's smallest variant generating approximately 1.1x more tokens.

Key takeaway

For NLP engineers deploying Gemma 4 31B, you should prioritize quantized versions like NVIDIA's NVFP4 or Red Hat AI's FP8 variants to significantly reduce memory footprint and improve inference speed. Be aware that models with quantized attention layers may exhibit a minor accuracy drop, especially on benchmarks like MMLU-Pro, but overall performance remains close to the original BF16 model. Choose a variant based on your specific hardware constraints and acceptable accuracy trade-offs.

Key insights

Quantization effectively compresses Gemma 4 31B, reducing memory and improving speed with minimal accuracy loss.

Principles

• Quantizing attention layers can slightly degrade accuracy.
• Quantization has little impact on token efficiency.

Method

Evaluated Gemma 4 31B quantized variants (NVFP4, INT4, FP8) for accuracy, efficiency, and memory using vLLM 0.19 on a B200 GPU.

In practice

• Consider NVFP4 or FP8 for strong performance.
• Quantized models below 30 GB typically quantize attention layers.

Topics

• Gemma 4 31B
• LLM Quantization
• FP8 Quantization
• NVFP4 Quantization
• INT4 Quantization

Best for: NLP Engineer, Machine Learning Engineer, AI Engineer, AI Scientist

Related on AIssential

• Gemma 4 31B vs Qwen3.5 27B: Inference Speed, Token-Efficiency, Accuracy, and Memory Consumption — Gemma 4 31B generally outperforms Qwen3.5 27B in accuracy and consistency, with better generalization.
• NVIDIA Introduces a 4-Bit Pretraining Methodology Using NVFP4, Validated on a 12B Hybrid Mamba-Transformer at 10T Token Horizon — NVIDIA achieved the first multi-trillion-token 4-bit pretraining, matching FP8 accuracy with a hybrid Mamba-Transformer.
• Gemma 4 31B and 26B A4B: Architecture and Memory Consumption — New Gemma 4 and Trinity-Large-Thinking models offer diverse architectures and memory footprints for large language model deployment.
• Gemma 4 is not your standard transformer — Gemma 4's architectural deviations suggest a re-evaluation of core transformer assumptions, particularly regarding token information flow.
• Gemma 4 MTP Local Test | Multi-Token Prediction of E2B using HuggingFace Transfomers | 🔴 Live — Gemma 4 models achieve 2-3x inference speedup via multi-token prediction using small drafter models and speculative decoding.
• See what 3 builders are making with Gemma 4 — Gemma 4's flexible, open models enable diverse on-device, vision-language, and large-context applications.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.