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GPT-OSS 20B Example Reports

The smallest model in the GPT-OSS family, offering good performance for routine research tasks with excellent speed and efficiency.

Model Details

  • Parameters: 20 Billion
  • Context: 131K tokens
  • Deployment: Self-hosted via vLLM
  • Best For: Quick research, drafts, routine documentation

Known Issues

  • Instruction Following: Difficulties with complex or non-traditional report formats

Available Reports

  • Psychological Effects of AI Tutoring

    Style: Academic psychological research
    Length: ~18,000 words

    AI education impacts, empirical analysis, and long-term effects.

    Read Report

  • Neuro-Pricing Insights

    Style: Technical business analysis
    Length: ~17,000 words

    Neuroscience-based pricing models and implementation strategies.

    Read Report

  • Surveillance in Hybrid Work

    Style: Professional workplace analysis
    Length: ~14,000 words

    Remote monitoring, privacy governance, and performance management.

    Read Report

Model Performance

Strengths

  • Fast Generation: Excellent speed for quick research
  • Resource Efficient: Runs on modest hardware
  • Good Structure: Maintains logical organization
  • Reliable Output: Consistent quality for standard tasks
  • Cost Effective: Minimal resource requirements

Best Use Cases

  • Quick research summaries
  • Initial drafts and outlines
  • Standard business reports
  • Routine documentation
  • Time-sensitive research tasks

Deployment Configuration

python -m vllm.entrypoints.openai.api_server \
    --model "/path/to/model/openai_gpt-oss-20b" \
    --tensor-parallel-size 4 \
    --port 5000 \
    --host 0.0.0.0 \
    --gpu-memory-utilization 0.9 \
    --served-model-name "localmodel" \
    --disable-log-requests \
    --disable-custom-all-reduce \
    --guided-decoding-backend "xgrammar"

Hardware Requirements

Resource Efficient

  • Minimum: 1x RTX 3090 (24GB VRAM)
  • Recommended: 2x RTX 3090 (48GB VRAM)
  • Actual Usage: ~25GB VRAM