Can I Run GPT-OSS on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026
Yes, but it is tight
Yes, but it is tight — GPT-OSS 20B at Q5_K_M needs about 15.8 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~118.6 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~118.6 tok/s
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RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 576 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
GPT-OSS on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 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 | ✓ Yes | 8K | ~118.6 tok/s | 14.8 GB |
| Q4_K_M | 13.6 GB | ✓ Yes | 64K | ~132.4 tok/s | 12.6 GB |
| Q3_K_M | 9.9 GB | ✓ Yes | 128K | ~164.6 tok/s | 8.9 GB |
| Q2_K | 7.9 GB | ✓ Yes | 128K | ~190 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 | ✓ Fits | ~132.4 tok/s |
What to watch out for
- Only ~0.2 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
- 1 larger variant of GPT-OSS does not fit and would need CPU offload or different hardware.
RTX 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 16 GB of VRAM on the NVIDIA GeForce RTX 4090 Laptop GPU at 576 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- 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 RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes, but it is tight — GPT-OSS 20B at Q5_K_M needs about 15.8 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~118.6 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of GPT-OSS should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q5_K_M — it needs about 15.8 GB of the 16 GB available, downloads as roughly 14.8 GB, and runs at an estimated 118.6 tokens/sec with up to 8K of context.
What limits GPT-OSS on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
- GPT-OSS on MacBook Pro M4 Max 128 GB
- GPT-OSS on MacBook Pro M4 Max 48 GB
- GPT-OSS on MacBook Pro M4 Pro 24 GB
- GPT-OSS on MacBook Air M4 16 GB
Other Models on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- Granite 3.0 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.0 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- IBM Granite 4.2 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)
- InternLM 3 on RTX 4090 Laptop (16 GB VRAM, 32 GB 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