作者： Jakub Rusinowski · 最后更新： 2024年11月26日
世界上最透明的LLM。OLMo 2公开一切：权重、训练代码、训练数据、评估套件和中间检查点。非常适合需要完全可审计AI的研究人员和合规敏感部署。在大多数基准测试中与Llama 3.1持平。
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | OLMo 2 7B Instruct, OLMo 2 13B Instruct |
| OLMo 2 7B Instruct | Min 5 GB VRAM · Q4_K_M · 4,096 ctx · ollama run olmo2:7b |
| OLMo 2 13B Instruct | Min 9 GB VRAM · Q4_K_M · 4,096 ctx · ollama run olmo2:13b |
The cheapest GPU that runs OLMo 2 locally (min 5 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run olmo2:7b
Minimum VRAM: 5 GB. For best results use Q4_K_M quantization.
OLMo 2 needs about 5 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: OLMo 2 7B Instruct (5 GB, Q4_K_M); OLMo 2 13B Instruct (9 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — OLMo 2 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.
Q4_K_M is the best balance of quality and VRAM for OLMo 2 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 olmo2:7b. This downloads OLMo 2 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.