Intern-Decision — InternLM 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
InternLM 基于 Qwen3.5 的多模态结构化决策模型，一次前向传播即可回答模式中的每个问题。
变体
Intern-Decision 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 |
|---|---|---|---|---|
| Intern-Decision 4B → 4B | ~3.6 GB | Not published | reference Python code |
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 Intern-Decision
安装 Ollama，然后拉取标签。
Served by reference Python code. This model does not run in Ollama.
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: Intern-Decision 4B推荐 GPU
目录中能在本地运行 Intern-Decision（至少 4 GB 显存）的最便宜 GPU 是 Intel Arc B570 (10 GB).
Intern-Decision — 常见问题
How much VRAM does Intern-Decision need?
Intern-Decision needs about 4 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Intern-Decision 4B (4 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Intern-Decision on an RTX 4090 (24 GB)?
Yes — Intern-Decision 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 Intern-Decision?
Q4_K_M is the best balance of quality and VRAM for Intern-Decision 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 Intern-Decision locally?
Intern-Decision is a decision model: it is called over an HTTP endpoint (/v1/systemone), not chatted with. Where a variant is available in Ollama 0.35 or newer, pull it with `ollama pull` and send requests to the local server; the others ship their own server. Each variant page shows the exact commands for that model.