Can I Run Gemma 3 on 24 GB system RAM?
Written by Jakub Rusinowski · Last updated March 12, 2025
Yes — comfortably
Yes, comfortably — Gemma 3 12B Instruct at Q6_K needs about 13.7 GB of the 19.2 GB usable on 24 GB system RAM, leaving ~5.5 GB spare and running at ~5.8 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~5.8 tok/s
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24 GB system RAM — what it gives a model
| Usable memory for models | 19.2 GB |
| Memory bandwidth | 90 GB/s |
Gemma 3 on 24 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 19.2 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 27.9 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 16.6 GB | ✓ Yes | 8K | ~4.7 tok/s | 12.8 GB |
| Q6_K | 13.7 GB | ✓ Yes | 16K | ~5.8 tok/s | 9.8 GB |
| Q5_K_M | 12.4 GB | ✓ Yes | 16K | ~6.6 tok/s | 8.5 GB |
| Q4_K_M | 11.1 GB | ✓ Yes | 16K | ~7.5 tok/s | 7.2 GB |
| Q3_K_M | 9 GB | ✓ Yes | 32K | ~9.9 tok/s | 5.1 GB |
| Q2_K | 7.8 GB | ✓ Yes | 32K | ~11.9 tok/s | 3.9 GB |
Which Gemma 3 sizes fit
| 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 | ~7.5 tok/s |
| Gemma 3 4B Instruct | 4.4 GB | ✓ Fits | ~21.4 tok/s |
| Gemma 3 1B Instruct | 2 GB | ✓ Fits | ~63.6 tok/s |
What to watch out for
- 1 larger variant of Gemma 3 does 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.
- 19.2 GB of the 24 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 24 GB system RAM?
Yes, comfortably — Gemma 3 12B Instruct at Q6_K needs about 13.7 GB of the 19.2 GB usable on 24 GB system RAM, leaving ~5.5 GB spare and running at ~5.8 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Gemma 3 should I use on 24 GB system RAM?
Q6_K — it needs about 13.7 GB of the 19.2 GB available, downloads as roughly 9.8 GB, and runs at an estimated 5.8 tokens/sec with up to 16K of context.
What limits Gemma 3 on 24 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 24 GB system RAM
- Gemma 3n on 24 GB system RAM
- Gemma 4 on 24 GB system RAM
- GLM-4.7 / GLM-Z1 on 24 GB system RAM
- GLM-5 / GLM-5.1 on 24 GB system RAM
- GLM-6 on 24 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