openai/parameter-golf resource

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5174 stars · 3277 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

An OpenAI-hosted challenge to train the best-performing language model that fits in a 16MB artifact and trains in under 10 minutes on 8xH100s, evaluated by bits-per-byte compression on FineWeb. It includes training code, leaderboard infrastructure, and compute grants to encourage creative architectures and compression techniques.

Use cases

• train the smallest language model that fits in 16MB
• compete on a parameter-constrained LLM training benchmark
• experiment with model compression and quantization-aware training
• learn efficient LLM training techniques like parameter tying and depth recurrence
• benchmark novel architectures under fixed compute limits
• apply for compute credits to train small models

When to choose

• you want a competitive benchmark for parameter-efficient LLM training
• you're exploring compression, QAT, or novel small-model architectures
• you want a structured challenge with a leaderboard and community

When to avoid

• you need a production-ready language model or inference library
• you lack access to H100-class GPUs and don't want to request grants
• you need general-purpose training tooling rather than a challenge codebase

Facets

learning-resource · maturity active

llm-training benchmarking machine-learning deep-learning large-language-models machine-learning gpu-computing tutorials python model-compression competition nanogpt quantization parameter-efficiency challenge gpu linux

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markdown · JSON · MCP: product_card(name="openai/parameter-golf")