Can I Run GPT-OSS on 16 GB system RAM?
作者： Jakub Rusinowski · 最后更新： 2026年8月15日
Yes
Yes — GPT-OSS 20B at Q3_K_M needs about 9.9 GB of the 12.8 GB usable on 16 GB system RAM (~2.9 GB spare), at ~37.2 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~37.2 tok/s
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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 | 42.8 GB | ✗ No | — | — | 41.8 GB |
| Q8_0 | 23.2 GB | ✗ No | — | — | 22.2 GB |
| Q6_K | 18.2 GB | ✗ No | — | — | 17.1 GB |
| Q5_K_M | 15.8 GB | ✗ No | — | — | 14.8 GB |
| Q4_K_M | 13.6 GB | ✗ No | — | — | 12.6 GB |
| Q3_K_M | 9.9 GB | ✓ Yes | 64K | ~37.2 tok/s | 8.9 GB |
| Q2_K | 7.9 GB | ✓ Yes | 128K | ~46.2 tok/s | 6.9 GB |
Which GPT-OSS sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| GPT-OSS 120B | 71.6 GB | ✗ Too large | — |
| GPT-OSS 20B | 13.6 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 9.9 GB of the 12.8 GB usable on 16 GB system RAM (~2.9 GB spare), at ~37.2 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 9.9 GB of the 12.8 GB available, downloads as roughly 8.9 GB, and runs at an estimated 37.2 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
- Granite 3.0 on 16 GB system RAM
- IBM Granite 4.1 on 16 GB system RAM
- IBM Granite 4.2 on 16 GB system RAM
- InternLM 3 on 16 GB system RAM
- LFM2.5 on 16 GB system RAM
GPT-OSS on GPUs
- GPT-OSS on NVIDIA GeForce RTX 5080
- GPT-OSS on NVIDIA GeForce RTX 5070 Ti
- GPT-OSS on NVIDIA GeForce RTX 5070
- GPT-OSS on NVIDIA GeForce RTX 5060 Ti 16GB