作者： Jakub Rusinowski · 最后更新： 2026年6月3日
Google第四代开源模型系列，2026年初发布。Gemma 4在保持友好硬件要求的同时，大幅提升推理、编程和多语言能力。1B和4B变体可在手机和入门GPU上运行；27B与70B级模型一争高下。
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | Gemma 4 E2B, Gemma 4 E4B, Gemma 4 26B-A4B, Gemma 4 31B, Gemma 4 12B (Unified) |
| Gemma 4 E2B | Min 4 GB VRAM · Q4 (QAT) · 128,000 ctx · ollama run gemma4:e2b |
| Gemma 4 E4B | Min 6 GB VRAM · Q4 (QAT) · 128,000 ctx · ollama run gemma4:e4b |
| Gemma 4 26B-A4B | Min 16 GB VRAM · Q4_K_M · 256,000 ctx · ollama run gemma4:26b-a4b |
| Gemma 4 31B | Min 20 GB VRAM · Q4_K_M · 256,000 ctx · ollama run gemma4:31b |
| Gemma 4 12B (Unified) | Min 8 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma4:12b |
The cheapest GPU that runs Gemma 4 locally (min 4 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run gemma4:e2b
Minimum VRAM: 4 GB. For best results use Q4_K_M quantization.
Gemma 4 needs about 4 GB VRAM at Q4 (QAT) quantization for its smallest variant. Variants: Gemma 4 E2B (4 GB, Q4 (QAT)); Gemma 4 E4B (6 GB, Q4 (QAT)); Gemma 4 26B-A4B (16 GB, Q4_K_M); Gemma 4 31B (20 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Gemma 4 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 (QAT).
Q4_K_M is the best balance of quality and VRAM for Gemma 4 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.
Install Ollama, then run: ollama run gemma4:e2b. This downloads Gemma 4 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.