Gemma is Google’s family of open-weight generative AI models. The current generation documented by Google is Gemma 4, which spans five model sizes and supports different combinations of text, images, video, and audio. You can use Gemma through developer tools or run compatible versions locally, but the right model depends on what you want to do and the memory available to your device.
What is Gemma?
Google describes Gemma as a family of generative AI models for tasks such as answering questions, summarizing, and reasoning. Its weights are available for developers to adapt and deploy. Google says the models permit responsible commercial use, subject to the applicable model terms and responsible-use requirements; “open weights” does not mean public domain or unrestricted use. Google’s Gemma documentation describes the current family.
Gemma is a model family, not a single model or a consumer game. When comparing advice or technical requirements, check the generation and variant: capabilities and resource needs differ across the lineup.
Which Gemma 4 model should you look at?
Google’s current overview lists five Gemma 4 sizes. Its descriptions indicate intended use and architecture, not independent comparative test results.
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| Model | Google’s description | Potential fit |
|---|---|---|
| E2B | Small effective-parameter model | Mobile, edge, or browser deployment |
| E4B | Small effective-parameter model | Mobile, edge, or browser deployment |
| 12B | Unified model for multimodal work | Work involving multiple supported input types |
| 26B A4B | Mixture-of-experts model | High-throughput reasoning |
| 31B | Dense model | Server-class performance with a local-execution target |
Those use cases are starting points, not guarantees. A model that fits a device’s memory may still be too slow or unsuitable for a particular task, so evaluate it with representative prompts and the runtime you intend to use.
What can Gemma 4 do?
Google’s Gemma 4 overview documents text and image processing across the family, video processing, and native audio input on E2B, E4B, and 12B. It also lists configurable thinking modes, built-in function calling, native system prompts, and multi-token prediction. These are documented capabilities; results on a specific task depend on the model, setup, and task, and should be evaluated directly. See Google’s Gemma 4 overview.
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Can you run Gemma locally?
Yes. Google identifies llama.cpp and LM Studio with QAT GGUF as local deployment options that support CPU, Apple Silicon, and consumer GPUs. Google also lists downloads through Kaggle and Hugging Face. The exact steps and supported model files depend on the chosen tool and release. Google’s Gemma documentation covers the family and downloads.
For local use, consider more than the model’s parameter count. Precision and quantization affect weight memory; the inference runtime and usable context window affect additional memory, and hardware also determines speed and power use. Google lists 128K context windows for small Gemma 4 models and 256K for medium models. Longer prompts and generated responses can require more memory for the context’s KV cache.
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How much memory does Gemma 4 need to load?
Google AI for Developers’ 2026 estimates below are approximate GPU or TPU memory-to-load figures. They include 20% overhead for loading additional items, but exclude memory used by supporting software and the context-window KV cache. They are not independent benchmarks or minimum GPU recommendations, and a device with exactly the listed capacity is not guaranteed to run a workload well. Google notes that deployment memory depends on the inference tool and environment.
| Gemma 4 model | BF16 | SFP8 | Q4_0 |
|---|---|---|---|
| E2B | 11.4 GB | 5.7 GB | 2.9 GB |
| E4B | 17.9 GB | 8.9 GB | 4.5 GB |
| 12B | 26.7 GB | 13.4 GB | 6.7 GB |
| 26B A4B | 57.7 GB | 28.8 GB | 14.4 GB |
| 31B | 69.9 GB | 34.9 GB | 17.5 GB |
Quantization lowers the listed loading estimate, but may change output quality. The figures do not account for the extra needs of a specific prompt, context length, runtime, or other applications using the same memory.
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What hardware should you choose for local inference?
Start with a particular model, quantization, runtime, and context length, then compare their combined memory needs with the memory your system can actually make available. A desktop GPU can help with workloads that benefit from GPU inference, but the table is not a shopping specification: system memory, software overhead, and the workload matter too. Google’s example of a consumer NVIDIA RTX 3090 concerns Gemma 3 27B QAT; it is not a universal recommendation for Gemma 4. If searching for hardware, “24GB graphics card for local AI inference” is a starting phrase, not a guarantee of compatibility, stock, or price.
Can you use Gemma commercially?
Google says Gemma models permit responsible commercial use. That permission remains subject to the applicable model terms and responsible-use requirements, so review the terms for the specific model and use case before deployment. Google also documents tuning and deployment workflows; tuning requires substantially more compute and memory than ordinary inference.
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