Open-weight local LLM

GPT-OSS (120B)

OpenAI flagship open-weight reasoning model. 128K context, strong tool use and Apache 2.0 licensing, now practical for 96GB+ local workstations via GGUF MXFP4.

Large-memory workstation 96 GB RAM MXFP4 High-end local reasoning
Parameters
117B (5.1B active)
Minimum RAM
96 GB
Model size
63 GB
Quantization
MXFP4

Can GPT-OSS (120B) run locally?

GPT-OSS (120B) needs a serious workstation with large unified memory or high VRAM.

Search for gpt-oss-120b in LM Studio or another GGUF-compatible runtime.

chatcodereasoningbeastgeneral

Install path

01
Check RAM fitMinimum 96 GB RAM. Start with the MXFP4 quant.
02
Load the modelSearch gpt-oss-120b in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Flagship OpenAI open-weight reasoning model
  • Apache 2.0 license
  • Near o4-mini-class reasoning target
  • 128K context
  • Strong coding and tool-use behavior

Limitations

  • Needs a 96GB+ workstation or single 80GB GPU class setup
  • Large MXFP4 GGUF download
  • Slow on CPU-only systems

Best use cases

  • High-end local reasoning
  • Coding agents
  • Tool-use workflows
  • Research comparison
  • Private workstation inference

Capability profile

speed
2
quality
10
coding
10
reasoning
10

Technical notes

Developer
OpenAI
License
Apache 2.0
Context window
131,072 tokens
Architecture
Sparse MoE Transformer with 117B total parameters, 5.1B active parameters, 128 experts and 128K context

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

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