Gemma 4 E4B It Assistant
Gemma 4 E4B It Assistant is a multimodal, open-source chat model built by Google DeepMind, designed for low-latency and on-device applications. It excels at text generation, coding, and reasoning, handling text and image inputs with audio support.
Input
Output
Context
131K
Max Output
-
Parameters
78.8M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Gemma 4 E4B It Assistant
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Gemma 4 E4B It Assistant
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Gemma 4 E4B It Assistant advertises a context window of 131,072 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Gemma 4 E4B It Assistant is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Gemma 4 E4B It Assistant accepts Text, Image and Audio and produces Text, Image and Audio. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Gemma 4 E4B It Assistant is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Gemma 4 E4B It Assistant is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.5 GB at INT8 for the weights and a default context, from the catalog's 78.8M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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