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Can the RTX 3060 12GB Run Gemma 3 12B? (✅ 7.5GB VRAM Needed, 2026)

Can the RTX 3060 12GB run Gemma 3 12B locally? Yes — runs great at Q4 (recommended). VRAM requirements, expected tokens/sec, the right quantization, and the cheapest GPU that fits if it doesn’t.

← Can It Run? — GPU compatibility matrix · Local LLMs guide

✅ Yes — runs great at Q4 (recommended)
Gemma 3 12B needs 7.5 GB VRAM at Q4. The RTX 3060 12GB has 12 GB.

💎 Gemma 3 12B (Google) is a 12B-parameter model for high-quality chat, vision, reasoning. Google’s efficient mid-size model with strong quality per GB. The question is whether the RTX 3060 12GB’s 12 GB of VRAM can hold it — here’s the exact math.

VRAM requirements

QuantizationVRAM neededRTX 3060 12GB
Q4_K_M (recommended)7.5 GB
Q8_0 (near-lossless)12.8 GB

Expected performance

Running Gemma 3 12B at Q4 on the RTX 3060 12GB, expect roughly ~25 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.

Headroom: 7.5 GB used of 12 GB leaves about 4.5 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.

About the RTX 3060 12GB

Architecture: Ampere · VRAM: 12 GB · Price: ~$300.

Good: 12GB VRAM at a budget price — the classic entry LLM card
Watch out: Older Ampere architecture, slower than Ada-gen cards

Check current RTX 3060 12GB price on Amazon →

Related checks

Other models on the RTX 3060 12GB

Gemma 3 12B on other GPUs

🎯 LLM Hardware Checker

Enter your exact GPU + RAM, see every model you can run.

💾 VRAM Requirements

Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.

Frequently asked questions

Can the RTX 3060 12GB run Gemma 3 12B?

Yes — runs great at Q4 (recommended). Gemma 3 12B needs 7.5 GB VRAM at Q4_K_M; the RTX 3060 12GB has 12 GB.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B needs about 7.5 GB at Q4_K_M (recommended) and 12.8 GB at Q8_0.

How many tokens per second will the RTX 3060 12GB get on Gemma 3 12B?

Roughly ~25 tokens/sec at Q4 in Ollama or llama.cpp.

About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.

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