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Gemma 4 now has five model sizes, not four: Google added 12B Unified after the initial March 2026 release. Start with your deployment target, required inputs, context length, and available memory; then test the remaining candidates on your actual coding tasks. E2B and E4B are the edge-oriented choices, 12B Unified adds audio support with a 256K context, 26B A4B is Google’s broad-purpose starting suggestion, and 31B is the largest dense option.
Which Gemma 4 models are available now?
The four-size framing describes Gemma 4’s initial release on March 31, 2026. Google’s release history lists E2B, E4B, 31B, and 26B A4B at launch, then adds 12B Unified on June 3, 2026. The current model card therefore lists five choices. Google’s release history and model card are the references for the current lineup.
| Model | Architecture and size | Context | Inputs | Good first candidate when… |
|---|---|---|---|---|
| E2B | Per-layer embeddings; 2.3B effective parameters, 5.1B including embeddings | 128K | Text, image, audio | Mobile or edge deployment is the main constraint. |
| E4B | Per-layer embeddings; 4.5B effective parameters, 8B including embeddings | 128K | Text, image, audio | A phone or laptop is the target and more capacity than E2B may help. |
| 12B Unified | Dense, encoder-free model; image patches and audio waveforms are projected into the LLM embedding space | 256K | Text, image, audio | A laptop, desktop, or small server can run it and audio or longer context is needed. |
| 26B A4B | Mixture of Experts; 25.2B total parameters, 3.8B active parameters | 256K | Text, image | You want a larger general-purpose model and can load its full expert weights. |
| 31B | Dense model; 30.7B parameters | 256K | Text, image | A large server deployment fits the budget and the task warrants evaluating the largest dense option. |
Google’s deployment guidance places E2B on mobile; E4B on mobile and laptops; 12B and 26B A4B on laptops, desktops, and small servers; and 31B on large servers or server clusters. These are intended platform categories, not assurances about latency or memory on a particular machine. Google’s get-started guide and model card describe the lineup.
How should you narrow the choice?
- Choose the deployment class. For phone or edge use, begin with E2B and E4B. On a laptop, desktop, or small server, consider 12B Unified and 26B A4B if the available memory permits. Include 31B for a large-server deployment.
- Match the input modalities. All five models accept text and images. Audio input is documented for E2B, E4B, and 12B Unified; the model card lists text and image for 26B A4B and 31B. Do not assume every size handles audio.
- Compare context limits against your workload. E2B and E4B have 128K context; the other three have 256K. A larger maximum context does not guarantee equal speed, memory demand, or output quality at every prompt length.
- Estimate memory for the precision and context you intend to use. Use the published estimates below as a starting point, then account for runtime, KV cache, and workload. In particular, 26B A4B’s active-parameter count does not tell you how much model weight memory must be loaded.
- Evaluate the candidates on the same coding task. Keep the prompt, inference settings, latency requirements, and failure criteria consistent. Test tasks representative of your own codebase rather than selecting by model size or a general benchmark alone.
How much memory does local inference need?
Google’s published figures below are estimates for model loading. Google says they include 20% overhead for loading additional items but exclude extra VRAM for supporting software and the context window. Longer prompts and generated responses add KV-cache memory, and actual requirements can vary by inference tool and environment. The values are from Google AI for Developers’ 2026 overview; they are not guarantees that a given system will run a model at a particular context length. Google’s Gemma overview explains the estimates.
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| Model | BF16 | SFP8 | Q4_0 | Mobile estimate | Mobile, text-only |
|---|---|---|---|---|---|
| E2B | 11.4 GB | 5.7 GB | 2.9 GB | 1.1 GB | 0.84 GB |
| E4B | 17.9 GB | 8.9 GB | 4.5 GB | 2.5 GB | 2.2 GB |
| 12B Unified | 26.7 GB | 13.4 GB | 6.7 GB | not stated (Google AI for Developers, 2026) | not stated (Google AI for Developers, 2026) |
| 26B A4B | 57.7 GB | 28.8 GB | 14.4 GB | not stated (Google AI for Developers, 2026) | not stated (Google AI for Developers, 2026) |
| 31B | 69.9 GB | 34.9 GB | 17.5 GB | not stated (Google AI for Developers, 2026) | not stated (Google AI for Developers, 2026) |
The table’s mobile columns are Google’s estimates for those model variants, not a promise of performance on any particular phone. Quantization can lower the weight-memory estimate, but the runtime and context still matter. For 26B A4B, 3.8B parameters are active at a time, but its 25.2B total expert parameters still need to be loaded; do not size memory from the active count alone.
Is 26B A4B the best model to start with?
Google’s guide says, “A good place to start is the Gemma 4 26B A4B model in the latest available version, which can be used for many tasks and has lower resource requirements.” That is Google’s general recommendation, not a universal result from a comparative coding test. Its memory needs, image-only input support, and deployment setting may make another model a better fit. If audio is required, consider E2B, E4B, or 12B Unified; if the model must run on a phone, begin with the E-series; if your server can accommodate it and the task merits evaluation, test 31B as well. Google’s guide gives the recommendation.
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What do published benchmark scores tell you?
Google DeepMind’s model card reports MMLU Pro results for instruction-tuned Gemma 4 models: E2B 60.0%, E4B 69.4%, 12B 77.2%, 26B A4B 82.6%, and 31B 85.2%. These are publisher-reported scores on that benchmark, not a prediction of results on your repository, coding workflow, latency target, or preferred inference setup. Use them as one published comparison point, not a replacement for evaluating your own tasks. Google DeepMind’s model card contains the benchmark results.
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Google labels Gemma 4 open-weight and lists Apache 2.0; it says the models can be downloaded from Kaggle and Hugging Face. Check the current model card and overview for the applicable license and access details before adopting a model.
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