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Gemma 3 12B needs 7.5 GB VRAM at Q4. The RTX 4060 Ti 16GB has 16 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 Ti 16GB’s 16 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4060 Ti 16GB |
|---|---|---|
| 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 Ti 16GB, expect roughly ~33 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 7.5 GB used of 16 GB leaves about 8.5 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 Ti 16GB
Architecture: Ada · VRAM: 16 GB · Price: ~$450.
Good: 16GB unlocks 14B models on a budget, low power draw
Watch out: Narrow 128-bit bus limits throughput vs pricier cards
Check current RTX 4060 Ti 16GB price on Amazon →
Related checks
Other models on the RTX 4060 Ti 16GB
🎯 LLM Hardware Checker
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💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4060 Ti 16GB run Gemma 3 12B?
Yes — runs at Q4 and full Q8 quality. Gemma 3 12B needs 7.5 GB VRAM at Q4_K_M; the RTX 4060 Ti 16GB has 16 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 Ti 16GB get on Gemma 3 12B?
Roughly ~33 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.