Qwen1.5-MoE: Matching 7B Model Performance with 1/3 Activated Parameters
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Qwen Team
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Qwen Team
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Terms in this piece · Glossary
mixture-of-experts — A model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Why it matters
Qwen1.5-mixture-of-expertsA model built from many specialist sub-networks where only a few activate per token, giving big-model capability at small-model running cost.Full definition →-A2.7B delivers roughly 7B-class quality while only activating 2.7B parameters, meaning cheaper and faster inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → for anyone self-hosting or running cost-sensitive LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → workloads. The fine-grained expert design is a useful reference point for teams weighing MoE architectures.