text model · gpt-oss · Windows
Can I run gpt-oss 20B on 8GB RAM Laptop (CPU/iGPU only)?
No. gpt-oss 20B needs ~13.2 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
Needs ~13.2 GB even at Q4_K_M, but only ~5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 8.2 GB: gpt-oss 20B needs roughly 13.2 GB at Q4_K_M and 8GB RAM Laptop (CPU/iGPU only) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs gpt-oss 20B is the Nvidia GeForce RTX 4060 Ti (16GB) at 16 GB. See gpt-oss 20B on Nvidia GeForce RTX 4060 Ti (16GB).
- Q4_K_M needed
- ~13.2 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
Which quant fits
- Parameters
- 21B (MoE, 3.6B active)
- Q4_K_M size
- 11.28 GB
- Q8_0 size
- 0.86 GB
- Context
- 128k
- Ollama tag
- gpt-oss:20b
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run gpt-oss 20B on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run gpt-oss 20B?
No. gpt-oss 20B needs ~13.2 GB even at Q4_K_M, but 8GB RAM Laptop (CPU/iGPU only) only has ~5 GB usable.
How much memory does gpt-oss 20B need?
8GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~11.28 GB; with KV cache and runtime overhead, budget ~13.2 GB at a 4k context. It is a Mixture-of-Experts model (21B total / 3.6B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run gpt-oss 20B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Embed this
[](https://localmodel.run/can-i-run/gpt-oss-20b/laptop-8gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.