Can I Run Gemma 4 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 3 czerwca 2026
Yes, but it is tight
Yes, but it is tight — Gemma 4 12B (Unified) at Q6_K needs about 11.3 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~0.7 GB before the runtime starts swapping. Expect ~24.5 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~24.5 tok/s
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RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 12 GB |
| Memory bandwidth | 360 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Gemma 4 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 25.4 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 14.2 GB | ✗ No | — | — | 12.8 GB |
| Q6_K | 11.3 GB | ✓ Yes | 32K | ~24.5 tok/s | 9.8 GB |
| Q5_K_M | 9.9 GB | ✓ Yes | 64K | ~27.9 tok/s | 8.5 GB |
| Q4_K_M | 8.7 GB | ✓ Yes | 64K | ~32.1 tok/s | 7.2 GB |
| Q3_K_M | 6.6 GB | ✓ Yes | 64K | ~42.9 tok/s | 5.1 GB |
| Q2_K | 5.4 GB | ✓ Yes | 64K | ~52.7 tok/s | 3.9 GB |
Which Gemma 4 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 4 31B | 21.4 GB | ✗ Too large | — |
| Gemma 4 26B-A4B | 17 GB | ✗ Too large | — |
| Gemma 4 12B (Unified) | 8.7 GB | ✓ Fits | ~32.1 tok/s |
| Gemma 4 E4B | 6.8 GB | ✓ Fits | ~44.9 tok/s |
| Gemma 4 E2B | 4.9 GB | ✓ Fits | ~64.5 tok/s |
What to watch out for
- Only ~0.7 GB of headroom at Q6_K: a longer context or a second application can push this into swapping.
- 2 larger variants of Gemma 4 do not fit and would need CPU offload or different hardware.
RTX 3060 12 GB desktop limitations
- The budget entry point to local AI: 12 GB runs 7–14B models well and nothing larger without offload.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 12 GB of VRAM on the NVIDIA GeForce RTX 3060 (12GB) at 360 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Gemma 4 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Yes, but it is tight — Gemma 4 12B (Unified) at Q6_K needs about 11.3 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~0.7 GB before the runtime starts swapping. Expect ~24.5 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Gemma 4 should I use on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Q6_K — it needs about 11.3 GB of the 12 GB available, downloads as roughly 9.8 GB, and runs at an estimated 24.5 tokens/sec with up to 32K of context.
What limits Gemma 4 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
- GLM-4.7 / GLM-Z1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
- GLM-5 / GLM-5.1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
- GPT-OSS on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
- Granite 3.0 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
- IBM Granite 4.1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
Gemma 4 on GPUs
- Gemma 4 on NVIDIA GeForce RTX 5090
- Gemma 4 on NVIDIA GeForce RTX 5080
- Gemma 4 on NVIDIA GeForce RTX 5070 Ti
- Gemma 4 on NVIDIA GeForce RTX 5070