Gemma 3n — Google DeepMind 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
Google移动优先的多模态模型系列。采用MatFormer嵌套架构——单个模型文件包含多个可按不同规格运行的子模型。处理文本、图像、音频和视频。无需联网即可在手机上运行。
变体
Gemma 3n 最小的变体在 Q4_K_M 下约需 4 GB 显存——量化权重加框架开销，不含 KV 缓存。
| 模型 | Q4 下显存 | 显存 | 上下文 | 运行 |
|---|---|---|---|---|
| Gemma 3n E2B → 2B | ~4.1 GB | 32,768 | ollama run gemma3n:e2b | |
| Gemma 3n E4B → 4B | ~5.5 GB | 32,768 | ollama run gemma3n:e4b |
显存为 Q4_K_M 下的量化权重加开销，与 GPU 与显存检测器使用同一引擎计算。
如何在本地运行 Gemma 3n
安装 Ollama，然后拉取标签。
ollama run gemma3n:e2b在上方选择一个尺寸，查看它自己的显存、速度估算和安装命令。
许可证
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
适用于: Gemma 3n E2B, Gemma 3n E4B推荐 GPU
目录中能在本地运行 Gemma 3n（至少 4 GB 显存）的最便宜 GPU 是 Intel Arc B570 (10 GB).
我的 GPU 能运行 Gemma 3n 吗？
Gemma 3n — 常见问题
How much VRAM does Gemma 3n need?
Gemma 3n needs about 4 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Gemma 3n E2B (4 GB, Q4_K_M); Gemma 3n E4B (6 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Gemma 3n on an RTX 4090 (24 GB)?
Yes — Gemma 3n runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
What quantization should I use for Gemma 3n?
Q4_K_M is the best balance of quality and VRAM for Gemma 3n in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run Gemma 3n with Ollama?
Install Ollama, then run: ollama run gemma3n:e2b. This downloads Gemma 3n and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.