VibeThinker — lokalny model AI od WeiboAI
Autor: Jakub Rusinowski · Ostatnia aktualizacja:
MIT-licensed reasoning fine-tunes that argue the case for small models: VibeThinker-3B scores 94.3 on AIME26, matching what DeepSeek V3.2 reaches at 671B. Trained with the Spectrum-to-Signal pipeline — explore solution diversity during SFT, then reinforce the correct signal with RL. Vendor-reported and contested; treat the headline comparison as a claim about a narrow benchmark family, not general capability.
Warianty
Najmniejszy wariant VibeThinker potrzebuje około 2 GB VRAM przy Q4_K_M — skwantyzowane wagi plus narzut frameworka, bez pamięci podręcznej KV.
| Model | VRAM przy Q4 | VRAM | Kontekst | Uruchom |
|---|---|---|---|---|
| VibeThinker 3B → 3B | ~2.7 GB | 131,072 | ollama run vibethinker | |
| VibeThinker 1.5B → 1.5B | ~1.7 GB | 131,072 | ollama run vibethinker |
Pamięć to skwantyzowane wagi plus narzut przy Q4_K_M, liczone tym samym silnikiem co sprawdzarka GPU i VRAM.
Jak uruchomić VibeThinker lokalnie
Zainstaluj Ollama, a potem pobierz tag.
ollama run vibethinkerWybierz rozmiar powyżej, aby zobaczyć jego VRAM, szacowaną szybkość i polecenie instalacji.
Licencja
Commercial use permitted. No usage restrictions beyond attribution.
Dotyczy: VibeThinker 3B, VibeThinker 1.5BPolecane GPU
Najtańsze GPU z naszego katalogu, które uruchomi VibeThinker lokalnie (min. 2 GB VRAM), to Intel Arc B570 (10 GB).
Czy uruchomię VibeThinker na moim GPU?
VibeThinker — najczęściej zadawane pytania
How much VRAM does VibeThinker need?
VibeThinker needs about 2 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: VibeThinker 3B (3 GB, Q4_K_M); VibeThinker 1.5B (2 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run VibeThinker on an RTX 4090 (24 GB)?
Yes — VibeThinker 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 VibeThinker?
Q4_K_M is the best balance of quality and VRAM for VibeThinker 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 VibeThinker with Ollama?
VibeThinker 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.