← Can It Run? — GPU compatibility matrix · Local LLMs guide
Gemma 3 12B needs 7.5 GB VRAM at Q4. The RTX 4060 8GB has 8 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 4060 8GB’s 8 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4060 8GB |
|---|---|---|
| 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 4060 8GB, expect roughly ~23 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 7.5 GB used of 8 GB leaves about 0.5 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 4060 8GB
Architecture: Ada · VRAM: 8 GB · Price: ~$300.
Good: Cheap, efficient Ada card, DLSS 3, low power draw
Watch out: Only 8GB VRAM — caps you at 7-8B models
Check current RTX 4060 8GB price on Amazon →
Related checks
Other models on the RTX 4060 8GB
🎯 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 4060 8GB run Gemma 3 12B?
Yes — runs great at Q4 (recommended). Gemma 3 12B needs 7.5 GB VRAM at Q4_K_M; the RTX 4060 8GB has 8 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 4060 8GB get on Gemma 3 12B?
Roughly ~23 tokens/sec at Q4 in Ollama or llama.cpp.