GoogleGemma 4 31B on NVIDIA GeForce RTX 3090
Yes — RTX 3090 handles Gemma 4 31B well at Q4_K_M — 35 tok/s. Solid daily-driver performance on 24 GB VRAM.
Model Size
30.7B
Device VRAM
24 GB
Bandwidth
936 GB/s
Quantization
Q4_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 31B on NVIDIA GeForce RTX 3090 at Q4_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q4_K_M | 35 tok/s | 200ms | ✓ Yes | Good | estimated |
Notes
Q4_K_M
30.7B model. Q4_K_M 19.6GB on 24GB 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 3090
NVIDIA GeForce RTX 3090 has 24 GB at 936 GB/s. Street price: $899.
See all models NVIDIA GeForce RTX 3090 can run →Builds with NVIDIA GeForce RTX 3090
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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.