Can Laptop · GeForce RTX 4090 Laptop · 32 GB RAM run gpt-oss-20b?
16 GB VRAM + 32 GB RAM · 576 GB/s memory bandwidth · gpt-oss-20b has 20.9B parameters (3.6B active per token).
Short answer
Yes, excellent for chat and comfortable or better for every workload we test.
Best quantization for chat: MXFP4 (11 GB of weights) on Ollama. Generation: 100–170 tok/s, leaving about 2.8 GB free for your other apps.
How it rates for each workload
Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.
| Workload | Verdict | Quant | Generation | First reply | Context | |
|---|---|---|---|---|---|---|
| Chatwith Open WebUI | Excellent | MXFP4 | 100–170 tok/s | under 1 s | 8K | Details |
| Coding questionswith Continue | Excellent | MXFP4 | 95–160 tok/s | ≈1 s | 16K | Details |
| Coding in a repositorywith Aider | Comfortable | MXFP4 | 85–140 tok/s | ≈3 s | 32K | Details |
| Agentic codingwith OpenCode | Comfortable | MXFP4 | 80–140 tok/s | ≈4 s | 32K | Details |
| Long documents (64K)with Open WebUI | Excellent | MXFP4 | 65–110 tok/s | ≈15 s | 64K | Details |
Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate
Get it running
Copy-paste commands for the MXFP4 build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.
ollama run hf.co/unsloth/gpt-oss-20b-GGUF:F16
Downloads 13.79 GB (F16). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.
Other models for Laptop · GeForce RTX 4090 Laptop · 32 GB RAM
The best-rated models for chat on this machine.
- Gemma 4 26B-A4B110–190 tok/sExcellent
- MiniCPM5 2B100–170 tok/sExcellent
- Qwen3.5 9B35–55 tok/sExcellent
- Qwen3.5 4B65–100 tok/sExcellent
- Gemma 4 12B30–45 tok/sExcellent
- GLM-4.6V-Flash (10B)35–55 tok/sExcellent
Cheapest machines for gpt-oss-20b
Hardware rated comfortable or better for chat, lowest price first.
- Mac mini M4 24 GB$999 · 25–35 tok/sExcellent
- Mac mini M6 24 GB$1,299 · 30–50 tok/sExcellent
- Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB$1,300 · 20–35 tok/sExcellent
- Laptop · Core Ultra 7 258V (Arc 140V) · 32 GB$1,300 · 18–30 tok/sExcellent
- Desktop PC · GeForce RTX 5060 Ti 16GB · 32 GB RAM$1,330 · ≈100 tok/sExcellent
- Desktop PC · GeForce RTX 4060 Ti 16GB · 32 GB RAM$1,350 · 50–85 tok/sExcellent
Different tool, context size or workload?
These pages use typical settings. The full check models your exact setup.