Qwen1.5 MoE A2.7B

Name: Qwen1.5 MoE A2.7B
Author: Qwen (Alibaba)

by Qwen (Alibaba)Qwen

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released February 20248K context≈ 6,144 words14.3B params

Qwen1.5 MoE A2.7B runs lean by design — it has 14.3B total parameters but only activates 2.7B at a time, using a mixture-of-experts architecture to keep inference costs low while maintaining reasonable capability. It handles general text tasks competently without demanding the compute of a dense model its size. The trade-off is that routing overhead and MoE-specific quirks can surface in some deployment environments.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Creative Writing

Moderate

Multilingual

Qwen1.5 MoE A2.7B

by Qwen (Alibaba)Qwen

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released February 20248K context≈ 6,144 words14.3B params

Qwen1.5 MoE A2.7B runs lean by design — it has 14.3B total parameters but only activates 2.7B at a time, using a mixture-of-experts architecture to keep inference costs low while maintaining reasonable capability. It handles general text tasks competently without demanding the compute of a dense model its size. The trade-off is that routing overhead and MoE-specific quirks can surface in some deployment environments.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Creative Writing

Moderate

Multilingual

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Dense ModelA neural network where all parameters are active for every input, in contrast to sparse architectures like mixture-of-experts that selectively activate different parts.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.RoutingThe mechanism that decides which specialized sub-networks (experts) should process each input in a mixture-of-experts model.Routing OverheadThe computational cost added by the mechanism that decides which experts should process each input in a mixture-of-experts model.

Qwen1.5 MoE A2.7B

Capabilities

Qwen1.5 MoE A2.7B

Capabilities

Benchmark Scores

Use Case Fit

Glossary