3. Hardware Requirements

Thanks to its highly optimized MoE architecture and MXFP4 quantization, gpt-oss-120b can be fully deployed on a single 80GB GPU (e.g., NVIDIA H100 or AMD MI300X). This dramatically lowers the barrier to entry for enterprise-grade self-hosting.

4. Key Features & Capabilities

• Configurable Reasoning Effort: The model supports dynamic compute scaling. You can set the reasoning effort to low (for fast, everyday tasks), medium, or high (for complex mathematics, coding, or deep logical deduction).
• Transparent Chain-of-Thought (CoT): Developers have full visibility into the model's internal reasoning tokens, making it easier to debug, audit, and align agentic behavior.
• Agentic Optimization: Natively trained to use external tools, execute Python code, and reliably produce structured outputs (like JSON schemas) without failing.
• Efficient Fine-Tuning: The 120B parameter model can be efficiently fine-tuned on a single node to specialize in domain-specific tasks (e.g., legal analysis, medical documentation, or proprietary coding languages).

5. Benchmarks & Performance

When configured to high reasoning effort, gpt-oss-120b achieves remarkable scores across core reasoning benchmarks:

• GPQA Diamond (Graduate-level reasoning): ~78.2%
• IFBench (Instruction Following): ~69.0%
• Speed: Capable of generating up to 265+ tokens per second on optimized hardware.