Google DeepMind · gemma3n
Can I run gemma3n?
Gemma 3n models are designed for efficient execution on everyday devices such as laptops, tablets or phones.
Parameter counts and memory belong to each build below. A family can contain several sizes and tasks; fitting in memory is not a quality ranking.
Calculate memory for a build and contextgemma3n system requirements
Memory needed at 4K context, counting model weights, KV cache, compute buffers and runtime overhead. Download sizes come from the official Ollama registry.
| Build | Quantization | Download | Memory needed |
|---|---|---|---|
| gemma3n:e2b-it-q4_K_MParameter count not stated in tag32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 5.2 GB | ~6.3 GB |
| gemma3n:e2b-it-q8_0Parameter count not stated in tag32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 6.2 GB | ~7.3 GB |
| gemma3n:e4b-it-q4_K_MParameter count not stated in tag32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 7.0 GB | ~8.3 GB |
| gemma3n:e2b-it-fp16Parameter count not stated in tag32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 8.3 GB | ~9.6 GB |
| gemma3n:e4b-it-q8_0Parameter count not stated in tag32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q8_0 | 8.9 GB | ~10.2 GB |
| gemma3n:e4b-it-fp16Parameter count not stated in tag32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | F16 | 12.8 GB | ~14.5 GB |
Showing the 6 smallest non-alias builds. Choose any build and change the context. A context above the build's documented maximum is a hypothetical calculation, not a supported setting.
How to run gemma3n locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull gemma3n:e2b-it-q4_K_M - 3.Check the source for the build's task. For text generation, run
ollama run gemma3n:e2b-it-q4_K_M. Base/text builds may need a prompt template; an instruct build is usually the appropriate starting point for a conversation.
Frequently asked questions about gemma3n
- How much VRAM do I need to run gemma3n?
- At 4K context, the smallest published build of gemma3n (Q4_K_M) needs roughly 6.3 GB of GPU memory once weights, KV cache, compute buffers and runtime overhead are counted. Bigger quantizations and longer context windows need more. It can also run partly or entirely on the CPU using system RAM, more slowly.
- Can I run gemma3n without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 6.3 GB at 4K context, plus headroom for your operating system. Check the build's runtime requirements. We have not measured its speed on your CPU.
- How big is the gemma3n download?
- The smallest published build is 5.2 GB. There are 6 builds in total, the largest being 12.8 GB. Leave some extra free disk space beyond the download itself.
- How do I run gemma3n locally?
- Install Ollama, then download a specific build with "ollama pull gemma3n:e2b-it-q4_K_M". For a build that supports text generation, use ollama run with the same tag. Check the model source for its task and prompt format.
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Or start from your memory: 8 GB VRAM, 12 GB VRAM, 16 GB VRAM, 24 GB VRAM or a 16 GB Mac.
Model data from the official Ollama registry · verified 13/10/2026