OpenAI has released an open-weight model called gpt-oss-20b, a medium-sized model with about 21 billion parameters designed for efficient on-device use and local inference. It operates with a Mixture-of-Experts (MoE) architecture, having 32 experts but activating 4 per token, resulting in 3.6 billion active parameters during each forward pass. This design grants strong reasoning and tool-use capabilities with relatively low memory requirements — it can run on systems with as little as 16GB of RAM. The model supports up to 128k tokens of context length, enabling it to handle very long inputs.
“gpt-oss-20b” achieves performance comparable to OpenAI’s o3-mini model across common benchmarks, including reasoning, coding, and function calling tasks. It leverages modern architectural features such as Pre-LayerNorm for training stability, Gated SwiGLU activations, and Grouped Query Attention for faster inference. This model is intended to provide strong real-world performance while being accessible for consumer hardware deployments. Both gpt-oss-20b and the larger gpt-oss-120b (117B parameters) models are released under the Apache 2.0 license, aiming to foster transparency, accessibility, and efficient usage by developers and researchers.
In summary:
- Parameters: ~21 billion total, 3.6 billion active per token
- Experts: 32 total, 4 active per token (Mixture-of-Experts)
- Context length: 128k tokens
- Runs with as little as 16GB memory
- Performance matches o3-mini benchmarks, strong at coding, reasoning, few-shot function calling
- Released open-weight under Apache 2.0 license for broad developer access
This model is a step toward more accessible powerful reasoning AI that can run efficiently on local or edge devices. Follow the link
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