Written by Jakub Rusinowski · Last updated March 12, 2025
Yes, but it is tight
Yes, but it is tight — Gemma 3 27B Instruct at Q6_K needs about 31.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving only ~0.7 GB before the runtime starts swapping. Expect ~45.8 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~45.8 tok/s
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 63.1 GB | ✗ No | — | — | 54 GB |
| Q8_0 | 37.8 GB | ✗ No | — | — | 28.7 GB |
| Q6_K | 31.3 GB | ✓ Yes | 8K | ~45.8 tok/s | 22.1 GB |
| Q5_K_M | 28.3 GB | ✓ Yes | 8K | ~51.1 tok/s | 19.1 GB |
| Q4_K_M | 25.4 GB | ✓ Yes | 8K | ~57.2 tok/s | 16.3 GB |
| Q3_K_M | 20.6 GB | ✓ Yes | 16K | ~71.9 tok/s | 11.5 GB |
| Q2_K | 18 GB | ✓ Yes | 16K | ~83.7 tok/s | 8.9 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 25.4 GB | ✓ Fits | ~57.2 tok/s |
| Gemma 3 12B Instruct | 11.1 GB | ✓ Fits | ~113.8 tok/s |
| Gemma 3 4B Instruct | 4.4 GB | ✓ Fits | ~223.8 tok/s |
| Gemma 3 1B Instruct | 2 GB | ✓ Fits | ~342.5 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, but it is tight — Gemma 3 27B Instruct at Q6_K needs about 31.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving only ~0.7 GB before the runtime starts swapping. Expect ~45.8 tok/s (estimated), with room for about 8,192 tokens of context.
Q6_K — it needs about 31.3 GB of the 32 GB available, downloads as roughly 22.1 GB, and runs at an estimated 45.8 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