Written by Jakub Rusinowski · Last updated March 12, 2025
Yes
Yes — Gemma 3 12B Instruct at Q6_K needs about 13.7 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.3 GB spare), at ~55.4 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~55.4 tok/s
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 27.9 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 16.6 GB | ✗ No | — | — | 12.8 GB |
| Q6_K | 13.7 GB | ✓ Yes | 8K | ~55.4 tok/s | 9.8 GB |
| Q5_K_M | 12.4 GB | ✓ Yes | 16K | ~61.7 tok/s | 8.5 GB |
| Q4_K_M | 11.1 GB | ✓ Yes | 16K | ~69.2 tok/s | 7.2 GB |
| Q3_K_M | 9 GB | ✓ Yes | 16K | ~87 tok/s | 5.1 GB |
| Q2_K | 7.8 GB | ✓ Yes | 16K | ~101.3 tok/s | 3.9 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 25.4 GB | ✗ Too large | — |
| Gemma 3 12B Instruct | 11.1 GB | ✓ Fits | ~69.2 tok/s |
| Gemma 3 4B Instruct | 4.4 GB | ✓ Fits | ~156.4 tok/s |
| Gemma 3 1B Instruct | 2 GB | ✓ Fits | ~285.4 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Gemma 3 12B Instruct at Q6_K needs about 13.7 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.3 GB spare), at ~55.4 tok/s (estimated), with room for about 8,192 tokens of context.
Q6_K — it needs about 13.7 GB of the 16 GB available, downloads as roughly 9.8 GB, and runs at an estimated 55.4 tokens/sec with up to 8K 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