Can I Run GPT-OSS on 16 GB system RAM?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes
Yes — GPT-OSS 20B at Q3_K_M needs about 9.7 GB of the 12.8 GB usable on 16 GB system RAM (~3.1 GB spare), at ~7.6 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~7.6 tok/s
16 GB system RAM — what it gives a model
| Usable memory for models | 12.8 GB |
| Memory bandwidth | 90 GB/s |
GPT-OSS on 16 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 12.8 GB? | Max context | Est. speed | Download |
|---|
| F16 | 41.2 GB | ✗ No | — | — | 40 GB |
| Q8_0 | 22.5 GB | ✗ No | — | — | 21.3 GB |
| Q6_K | 17.6 GB | ✗ No | — | — | 16.4 GB |
| Q5_K_M | 15.4 GB | ✗ No | — | — | 14.2 GB |
| Q4_K_M | 13.3 GB | ✗ No | — | — | 12.1 GB |
| Q3_K_M | 9.7 GB | ✓ Yes | 64K | ~7.6 tok/s | 8.5 GB |
| Q2_K | 7.8 GB | ✓ Yes | 64K | ~9.7 tok/s | 6.6 GB |
Which GPT-OSS sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| GPT-oss 120B | 73.9 GB | ✗ Too large | — |
| GPT-OSS 20B | 13.3 GB | ✗ Too large | — |
What to watch out for
- 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.
- 2 larger variants of GPT-OSS 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.
- 12.8 GB of the 16 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 GPT-OSS on 16 GB system RAM?
Yes — GPT-OSS 20B at Q3_K_M needs about 9.7 GB of the 12.8 GB usable on 16 GB system RAM (~3.1 GB spare), at ~7.6 tok/s (estimated), with room for about 65,536 tokens of context.
Which quantization of GPT-OSS should I use on 16 GB system RAM?
Q3_K_M — it needs about 9.7 GB of the 12.8 GB available, downloads as roughly 8.5 GB, and runs at an estimated 7.6 tokens/sec with up to 64K of context.
What limits GPT-OSS on 16 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 16 GB system RAM
GPT-OSS on GPUs
What This Model Is Good At
Model & Tools
← Can I Run It? | GPT-OSS model page | Check your hardware