Apertus — EPFL / ETH Zurich / CSCS 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
瑞士的开放模型，来自EPFL、苏黎世联邦理工学院和瑞士国家超级计算中心。在约15万亿token上训练，覆盖1000多种语言，权重、数据和流程均以Apache-2.0许可公开——把欧洲的主权论点落到了实处。
变体
Apertus 最小的变体在 Q4_K_M 下约需 6 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 |
|---|---|---|---|---|
| Apertus 8B → 8B | ~5.6 GB | 65,536 | ollama run apertus | |
| Apertus 70B → 70B | ~43.1 GB | 65,536 | ollama run apertus |
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 Apertus
安装 Ollama，然后拉取标签。
ollama run apertus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
Commercial use permitted. No usage restrictions beyond attribution.
适用于: Apertus 8B, Apertus 70B推荐 GPU
目录中能在本地运行 Apertus（至少 6 GB 显存）的最便宜 GPU 是 Intel Arc B570 (10 GB).
Apertus — 常见问题
How much VRAM does Apertus need?
Apertus needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Apertus 8B (6 GB, Q4_K_M); Apertus 70B (43 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Apertus on an RTX 4090 (24 GB)?
Yes — Apertus 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 Apertus?
Q4_K_M is the best balance of quality and VRAM for Apertus 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 Apertus with Ollama?
Apertus has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.