Written by Jakub Rusinowski · Last updated April 1, 2025
Yes — comfortably
Yes, comfortably — Gemma 3n E4B at Q8_0 needs about 10.3 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~13.7 GB spare and running at ~112 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~112 tok/s
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 17.6 GB | ✓ Yes | 32K | ~70.9 tok/s | 15.7 GB |
| Q8_0 | 10.3 GB | ✓ Yes | 32K | ~112 tok/s | 8.3 GB |
| Q6_K | 8.4 GB | ✓ Yes | 32K | ~131.7 tok/s | 6.4 GB |
| Q5_K_M | 7.5 GB | ✓ Yes | 32K | ~143.4 tok/s | 5.6 GB |
| Q4_K_M | 6.7 GB | ✓ Yes | 32K | ~156.4 tok/s | 4.7 GB |
| Q3_K_M | 5.3 GB | ✓ Yes | 32K | ~184.7 tok/s | 3.3 GB |
| Q2_K | 4.5 GB | ✓ Yes | 32K | ~205.1 tok/s | 2.6 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3n E4B | 6.7 GB | ✓ Fits | ~156.4 tok/s |
| Gemma 3n E2B | 5.1 GB | ✓ Fits | ~217.1 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Gemma 3n E4B at Q8_0 needs about 10.3 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~13.7 GB spare and running at ~112 tok/s (estimated), with room for about 32,768 tokens of context.
Q8_0 — it needs about 10.3 GB of the 24 GB available, downloads as roughly 8.3 GB, and runs at an estimated 112 tokens/sec with up to 32K 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