GPTQ

gpt-oss-120b on AMD/Nvidia GPU Quantized GGUF Step-by-Step

By June 30, 2026No Comments

gpt-oss-120b on AMD/Nvidia GPU Quantized GGUF Step-by-Step

To get this model running locally in no time, utilize the built-in WSL tools.

Refer to the action plan below to initialize the model.

The download manager will automatically pull several gigabytes of data.

You don’t need to tweak anything; the installer picks the highest performing setup.

🔗 SHA sum: 0c50e68bfc4241e8c17ff331e7b048c7 | Updated: 2026-06-29



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: enough space for background apps and OS overhead
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

The gpt-oss-120b is an open‑source large language model featuring 120 billion parameters, built to enable transparent research and commercial deployment. It employs a mixture‑of‑experts architecture that balances inference efficiency with high contextual coherence across diverse tasks. The model supports multiple languages and incorporates built‑in safety alignments to reduce hallucinations and improve reliability. Benchmarks show it outperforms many 70‑billion‑parameter systems on reasoning tasks while consuming less computational power than comparable 175‑billion‑parameter models. A dedicated community hub provides pre‑trained checkpoints, fine‑tuning scripts, and comprehensive documentation for developers and researchers.

Parameters 120 billion
Training Data Web‑scale corpora in multiple languages
Inference Latency ≈120 ms per 512‑token sequence on GPU
Model Size ≈180 GB (float16)
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