Can I Run Gemma 3 on 8 GB system RAM?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 marca 2025
Yes
Yes — Gemma 3 4B Instruct at Q8_0 needs about 5.4 GB of the 6.4 GB usable on 8 GB system RAM (~1 GB spare), at ~14.4 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~14.4 tok/s
See what else this hardware can run →
lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)
Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.
8 GB system RAM — what it gives a model
| Usable memory for models | 6.4 GB |
| Memory bandwidth | 90 GB/s |
Gemma 3 on 8 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 6.4 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 9.2 GB | ✗ No | — | — | 8 GB |
| Q8_0 | 5.4 GB | ✓ Yes | 32K | ~14.4 tok/s | 4.3 GB |
| Q6_K | 4.4 GB | ✓ Yes | 64K | ~18.2 tok/s | 3.3 GB |
| Q5_K_M | 4 GB | ✓ Yes | 64K | ~20.7 tok/s | 2.8 GB |
| Q4_K_M | 3.6 GB | ✓ Yes | 128K | ~23.7 tok/s | 2.4 GB |
| Q3_K_M | 2.9 GB | ✓ Yes | 128K | ~31.7 tok/s | 1.7 GB |
| Q2_K | 2.5 GB | ✓ Yes | 128K | ~38.8 tok/s | 1.3 GB |
Which Gemma 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 18.4 GB | ✗ Too large | — |
| Gemma 3 12B Instruct | 9.1 GB | ✗ Too large | — |
| Gemma 3 4B Instruct | 3.6 GB | ✓ Fits | ~23.7 tok/s |
| Gemma 3 1B Instruct | 1.5 GB | ✓ Fits | ~84.6 tok/s |
What to watch out for
- Only ~1 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
- 2 larger variants of Gemma 3 do not fit and would need CPU offload or different hardware.
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 6.4 GB of the 8 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- 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 3 on 8 GB system RAM?
Yes — Gemma 3 4B Instruct at Q8_0 needs about 5.4 GB of the 6.4 GB usable on 8 GB system RAM (~1 GB spare), at ~14.4 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Gemma 3 should I use on 8 GB system RAM?
Q8_0 — it needs about 5.4 GB of the 6.4 GB available, downloads as roughly 4.3 GB, and runs at an estimated 14.4 tokens/sec with up to 32K of context.
What limits Gemma 3 on 8 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 8 GB system RAM
- Gemma 3n on 8 GB system RAM
- Gemma 4 on 8 GB system RAM
- GLM-4.7 / GLM-Z1 on 8 GB system RAM
- GLM-5 / GLM-5.1 on 8 GB system RAM
- Granite 3.0 on 8 GB system RAM
Gemma 3 on GPUs
- Gemma 3 on NVIDIA GeForce RTX 5090
- Gemma 3 on NVIDIA GeForce RTX 5080
- Gemma 3 on NVIDIA GeForce RTX 5070 Ti
- Gemma 3 on NVIDIA GeForce RTX 5070