MXFP4 GGUF. This is the model's native precision - GPT-OSS was trained in MXFP4, so no further quantization is needed or recommended. Re-quantizing would only lose quality.

Works with llama.cpp, LM Studio, Ollama, and anything else that loads GGUFs.

Downloads

File	Size
GPTOSS-120B-Uncensored-HauhauCS-Aggressive-MXFP4.gguf	61 GB

Specs

117B total parameters, ~5.1B active per forward pass (MoE: 128 experts, top-4 routing)
128K context
Based on openai/gpt-oss-120b

Recommended Settings

temperature: 1.0
top_k: 40
Everything else (top_p, min_p, repeat penalty, etc.) should be disabled - some clients enable these by default, turn them off

Required flag: --jinja to enable the Harmony response format (the model won't work correctly without it).

For llama.cpp:

llama-server -m model.gguf --jinja -fa -b 2048 -ub 2048

LM Studio

Compatible with Reasoning Effort custom buttons. To use them, put the model in:

LM Models\lmstudio-community\gpt-oss-120b-GGUF\

Hardware

Fits in ~61GB VRAM. Single H100 or equivalent. For lower VRAM, use --n-cpu-moe N in llama.cpp to offload MoE layers to CPU.

jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive

GPTOSS-120B-Uncensored-HauhauCS-Aggressive

Format

Downloads

Specs

Recommended Settings

LM Studio

Hardware

jaromer/
GPTOSS-120B-Uncensored-HauhauCS-Aggressive