Intern-Decision — local AI model by InternLM

Written by Jakub Rusinowski · Last updated

InternLM's multimodal structured decision models on Qwen3.5 that answer every question in a schema from one forward pass.

Variants

The smallest Intern-Decision variant needs about 4 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.

ModelVRAM
Intern-Decision 4B →
4B
~3.6 GB

Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.

How to run Intern-Decision locally

Install Ollama, then pull the tag.

Served by reference Python code. This model does not run in Ollama.

Pick a size above for its own VRAM figure, speed estimate and install command.

Licence

Apache-2.0Commercial use permitted

Commercial use permitted. No usage restrictions beyond attribution.

Applies to: Intern-Decision 4B

Recommended GPU

The cheapest catalogued GPU that runs Intern-Decision locally (min 4 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.

Intern-Decision — frequently asked questions

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.