gemma 3n E4B it

Name: gemma 3n E4B it
Author: Google

by Googlegemma

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

Released June 202533K context≈ 24,576 words4B params

Gemma 3n E4B runs efficiently on-device by using a selective parameter activation approach — not all 4 billion parameters fire for every token, keeping memory and compute lean. It handles both text and images with a practical, no-frills style suited to constrained environments. The trade-off is that its reasoning depth reflects its compact footprint.

Capabilities

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

Reasoning & Logic

Moderate

Factual Knowledge

gemma 3n E4B it

by Googlegemma

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

Released June 202533K context≈ 24,576 words4B params

Gemma 3n E4B runs efficiently on-device by using a selective parameter activation approach — not all 4 billion parameters fire for every token, keeping memory and compute lean. It handles both text and images with a practical, no-frills style suited to constrained environments. The trade-off is that its reasoning depth reflects its compact footprint.

Capabilities

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

Reasoning & Logic

Moderate

Factual Knowledge

Glossary

On-DeviceA model designed to run directly on a user's device (phone, laptop, etc.) rather than requiring a remote server.Parameter ActivationThe process of selectively using only a subset of a model's total parameters during inference, reducing computational cost while maintaining performance.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.Selective Parameter ActivationA technique where only a subset of a model's weights are used for each input, rather than activating all parameters, which reduces memory usage and speeds up inference.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary