Written by Jakub Rusinowski · Last updated April 1, 2025
Yes
Yes — Gemma 3n E4B at Q8_0 needs about 10.3 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.7 GB spare), at ~50.9 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~50.9 tok/s
| Usable memory for models | 12 GB |
| Memory bandwidth | 360 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 17.6 GB | ✗ No | — | — | 15.7 GB |
| Q8_0 | 10.3 GB | ✓ Yes | 16K | ~50.9 tok/s | 8.3 GB |
| Q6_K | 8.4 GB | ✓ Yes | 32K | ~61.9 tok/s | 6.4 GB |
| Q5_K_M | 7.5 GB | ✓ Yes | 32K | ~68.8 tok/s | 5.6 GB |
| Q4_K_M | 6.7 GB | ✓ Yes | 32K | ~76.7 tok/s | 4.7 GB |
| Q3_K_M | 5.3 GB | ✓ Yes | 32K | ~95.4 tok/s | 3.3 GB |
| Q2_K | 4.5 GB | ✓ Yes | 32K | ~110.1 tok/s | 2.6 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3n E4B | 6.7 GB | ✓ Fits | ~76.7 tok/s |
| Gemma 3n E2B | 5.1 GB | ✓ Fits | ~119.3 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Gemma 3n E4B at Q8_0 needs about 10.3 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.7 GB spare), at ~50.9 tok/s (estimated), with room for about 16,384 tokens of context.
Q8_0 — it needs about 10.3 GB of the 12 GB available, downloads as roughly 8.3 GB, and runs at an estimated 50.9 tokens/sec with up to 16K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving