Nex-N1 — lokalny model AI od Nex-AGI
Autor: Jakub Rusinowski · Ostatnia aktualizacja:
Nex-AGI's first agentic series, and the one that set the pattern: take someone else's open base weights, post-train them for tool use and autonomy, publish the result. The flagship is a DeepSeek-V3.1 post-train scoring 80.2 on tau2-Bench and 70.6 on SWE-bench Verified. Superseded by Nex-N2, but kept because the question 'what does a 671B agentic post-train need?' has a factual answer that does not change when the model stops being recommended.
Warianty
Najmniejszy wariant Nex-N1 potrzebuje około 406 GB VRAM przy Q4_K_M — skwantyzowane wagi plus narzut frameworka, bez pamięci podręcznej KV.
| Model | VRAM przy Q4 | VRAM | Kontekst | Uruchom |
|---|---|---|---|---|
| DeepSeek-V3.1-Nex-N1 671B → 671B (37B active) | ~405.9 GB | 128,000 | ollama run nex-n1 |
Pamięć to skwantyzowane wagi plus narzut przy Q4_K_M, liczone tym samym silnikiem co sprawdzarka GPU i VRAM.
Jak uruchomić Nex-N1 lokalnie
Zainstaluj Ollama, a potem pobierz tag.
ollama run nex-n1Wybierz rozmiar powyżej, aby zobaczyć jego VRAM, szacowaną szybkość i polecenie instalacji.
Licencja
Commercial use permitted. No usage restrictions beyond attribution.
Dotyczy: DeepSeek-V3.1-Nex-N1 671BCzy uruchomię Nex-N1 na moim GPU?
Nex-N1 — najczęściej zadawane pytania
How much VRAM does Nex-N1 need?
Nex-N1 needs about 406 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: DeepSeek-V3.1-Nex-N1 671B (406 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Nex-N1 on an RTX 4090 (24 GB)?
Nex-N1's smallest variant needs about 406 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.
What quantization should I use for Nex-N1?
Q4_K_M is the best balance of quality and VRAM for Nex-N1 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 Nex-N1 with Ollama?
Nex-N1 has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.