text model · gpt-oss · Windows
Can I run gpt-oss 120B on Nvidia GeForce RTX 4060 Ti (16GB)?
No. gpt-oss 120B needs ~62.4 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
Needs ~62.4 GB even at Q4_K_M, but only ~15 GB is usable.
- Q4_K_M needed
- ~62.4 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Parameters
- 117B (MoE, 5.1B active)
- Q4_K_M size
- 59.03 GB
- Context
- 128k
- Ollama tag
- gpt-oss:120b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run gpt-oss 120B on other hardware
FAQ
Can Nvidia GeForce RTX 4060 Ti (16GB) run gpt-oss 120B?
No. gpt-oss 120B needs ~62.4 GB even at Q4_K_M, but Nvidia GeForce RTX 4060 Ti (16GB) only has ~15 GB usable.
How much memory does gpt-oss 120B need?
Nvidia GeForce RTX 4060 Ti (16GB) does not have enough memory. At Q4_K_M the weights are ~59.03 GB; with KV cache and runtime overhead, budget ~62.4 GB at a 4k context. It is a Mixture-of-Experts model (117B total / 5.1B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run gpt-oss 120B 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.
Sources
Memory figures are estimates. See methodology.