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text model · gpt-oss · Windows

Can I run gpt-oss 20B on Nvidia GeForce RTX 4090 (24GB)?

Compatibility verdict VRAM threshold engine
Yes, it runs GPU accelerated

Yes. gpt-oss 20B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~13.2 GB of ~23 GB usable).

Needs ~13.2 GB Device usable ~23 GB

Runs at Q4_K_M using ~13.2 GB of ~23 GB usable. You have room for Q8_0 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4090 (24GB) leaves ~9.8 GB of headroom, room to step up to Q8_0 for higher quality.

Q4_K_M needed
~13.2 GB
Usable on device
~23 GB
Device memory
24 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~23 GB usable
Q2_K
~10.7 GB
Q3_K_M
~12.2 GB
Q4_K_M
~13.2 GB
Q5_K_M
~16.9 GB
Q6_K
~19.1 GB
Q8_0
~2.8 GB
FP16
~43.9 GB
The line marks Nvidia GeForce RTX 4090 (24GB)'s ~23 GB budget; rungs past it are too large.

Run it

Install commands Windows

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run gpt-oss:20b
llama.cpp
$ llama-cli -hf ggml-org/gpt-oss-20b-GGUF:Q4_K_M
LM Studio
$ lms get ggml-org/gpt-oss-20b-GGUF
Model gpt-oss
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
Full gpt-oss 20B requirements →
Device Windows
Memory
24 GB vram
Usable for weights
~23 GB
Power draw
~450 W
Best runtime
vLLM (Linux) / Ollama (CUDA)
Best models for Nvidia GeForce RTX 4090 (24GB) →

You could also run

Run gpt-oss 20B on other hardware

FAQ

Can Nvidia GeForce RTX 4090 (24GB) run gpt-oss 20B?

Yes. gpt-oss 20B runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~13.2 GB of ~23 GB usable).

How much memory does gpt-oss 20B need?

Nvidia GeForce RTX 4090 (24GB) has room to spare. 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.

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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.