GoogleGemma 4 31B on NVIDIA GeForce RTX 5080
RTX 5080 cannot run Gemma 4 31B. 16 GB VRAM is insufficient at any quantization level.
Model Size
30.7B
Device VRAM
16 GB
Bandwidth
960 GB/s
Quantization
Q3_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 31B on NVIDIA GeForce RTX 5080 at Q3_K_M. This pairing has limitations — check the rating and notes below.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q3_K_M | 22 tok/s | 364ms | ✗ Offload | Good | estimated |
Notes
Q3_K_M
30.7B model. Q3_K_M 14.59GB on 16GB discrete_gpu.
About Gemma 4 31B
Gemma 4 31B (30.7B) is a chat, coding, reasoning, multi-purpose model. Google's flagship open-weight model. Dense 30.7B parameters with 256K context. Benchmarks: 89.2% AIME 2026, 85.2% MMLU Pro, 84.3% GPQA Diamond, 80.0% LiveCodeBench v6, 86.4% agentic tool use. Supports text, image, and video input. Fits on a single RTX 4090 at Q4 or dual 16 GB GPUs. Direct successor to Gemma 3 27B with substantially better reasoning. Apache 2.0 licensed.
View all Gemma 4 31B hardware options →About NVIDIA GeForce RTX 5080
NVIDIA GeForce RTX 5080 has 16 GB at 960 GB/s. Street price: $1,099.
See all models NVIDIA GeForce RTX 5080 can run →Estimate method: Estimated from model architecture, quantization size, and device bandwidth. Reference hardware source: huggingface.co (2026-04-04)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.