Autor: Jakub Rusinowski · Ostatnia aktualizacja: 8 września 2026
Nex-AGI's Apache-2.0 agentic pair, post-trained on Qwen3.5 and built around what Nex calls Agentic Thinking: Adaptive Thinking lets the model choose when and how deeply to reason, Coherent Thinking keeps one reasoning style across chat, tool calls and terminal work. Pro reaches 75.3 on Terminal-Bench 2.1 and 90.7 on GPQA Diamond. The 35B mini is the size that matters for local use — 21.9 GB at Q4_K_M with 3B active parameters per token. This is the generation whose parameter counts Nex states outright.
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | Nex-N2 mini, Nex-N2 Pro |
| Nex-N2 mini | Min 22 GB VRAM · Q4_K_M · 262,144 ctx · |
| Nex-N2 Pro | Min 240 GB VRAM · Q4_K_M · 262,144 ctx · |
The cheapest GPU that runs Nex-N2 locally (min 22 GB VRAM) is the AMD Radeon RX 7900 XTX (24 GB).
Install Ollama then run: ollama run
Minimum VRAM: 22 GB. For best results use Q4_K_M quantization.
Nex-N2 needs about 22 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Nex-N2 mini (22 GB, Q4_K_M); Nex-N2 Pro (240 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Nex-N2 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 Nex-N2 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 . This downloads Nex-N2 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.