Can I Run Llama 3.1 Family on 8 GB system RAM?
Superseded model. Llama 3.1 Family has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point.
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Written by Jakub Rusinowski · Last updated July 23, 2024
Yes
Yes — Llama 3.1 8B Instruct at Q3_K_M needs about 5.3 GB of the 6.4 GB usable on 8 GB system RAM (~1.1 GB spare), at ~16.4 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~16.4 tok/s
8 GB system RAM — what it gives a model
| Usable memory for models | 6.4 GB |
| Memory bandwidth | 90 GB/s |
Llama 3.1 Family on 8 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 6.4 GB? | Max context | Est. speed | Download |
|---|
| F16 | 17.9 GB | ✗ No | — | — | 16 GB |
| Q8_0 | 10.4 GB | ✗ No | — | — | 8.5 GB |
| Q6_K | 8.4 GB | ✗ No | — | — | 6.6 GB |
| Q5_K_M | 7.5 GB | ✗ No | — | — | 5.7 GB |
| Q4_K_M | 6.7 GB | ✗ No | — | — | 4.8 GB |
| Q3_K_M | 5.3 GB | ✓ Yes | 16K | ~16.4 tok/s | 3.4 GB |
| Q2_K | 4.5 GB | ✓ Yes | 16K | ~20.3 tok/s | 2.6 GB |
What to watch out for
- Only ~1.1 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
- Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 1 larger variant of Llama 3.1 Family 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.
- 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 Llama 3.1 Family on 8 GB system RAM?
Yes — Llama 3.1 8B Instruct at Q3_K_M needs about 5.3 GB of the 6.4 GB usable on 8 GB system RAM (~1.1 GB spare), at ~16.4 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Llama 3.1 Family should I use on 8 GB system RAM?
Q3_K_M — it needs about 5.3 GB of the 6.4 GB available, downloads as roughly 3.4 GB, and runs at an estimated 16.4 tokens/sec with up to 16K of context.
What limits Llama 3.1 Family 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
Llama 3.1 Family on GPUs
What This Model Is Good At
Model & Tools
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