GoogleGemma 4 E4B IT
Gemma 4 E 4B It is a chat model developed by Google, featuring a context window of 131,072 tokens and handling text inputs. It is genuinely best at tasks like text generation, coding, and reasoning, thanks to its Dense and Mixture-of-Experts architectures.
Input
Output
Context
131K
Max Output
-
Parameters
8B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
Estimated GPU requirements for Gemma 4 E4B IT
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
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Gemma 4 E4B IT advertises a context window of 131,072 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Gemma 4 E4B IT 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 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.
Gemma 4 E4B IT has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Gemma 4 E4B IT is shown as not available rather than estimated.
Not on the managed pool today; Gemma 4 E4B IT 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 8.3 GB at INT8 for the weights and a default context, from the catalog's 8B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 6, 2026
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