Written by Jakub Rusinowski · Last updated July 10, 2026
A powerful open-source model from China's Shanghai AI Lab. InternLM 3 excels at bilingual Chinese-English tasks, coding, and long document analysis. Particularly strong in math and STEM reasoning, making it the top open choice for Chinese language applications.
| InternLM 3 8B Instruct | Min 6 GB VRAM · Q4_K_M · 32,768 ctx · ollama run internlm3:8b |
| InternLM 3 20B Instruct | Min 13 GB VRAM · Q4_K_M · 32,768 ctx · ollama run internlm3:20b |
The cheapest GPU that runs InternLM 3 locally (min 6 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run internlm3:8b
Minimum VRAM: 6 GB. For best results use Q4_K_M quantization.
InternLM 3 needs about 6 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: InternLM 3 8B Instruct (6 GB, Q4_K_M); InternLM 3 20B Instruct (13 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — InternLM 3 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 InternLM 3 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 internlm3:8b. This downloads InternLM 3 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.