Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026
Alibaba's August 2026 wave. The 27B is a natively multimodal dense model under Apache 2.0 with a 262K context that extends to 1M via YaRN — small enough for one consumer card, which is the combination most of this site's visitors are looking for. Qwen3.8-Max sits at the other extreme: 2.4T parameters with roughly 95B active, open-weighted under its own licence. Qwen3.8-Flash-Next is neither — an experimental preview of the Qwen4 architecture, 180B resident but only 6B active, under the Qwen Community License rather than Apache.
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | Qwen3.8 27B |
Custom Open-Weight | Commercial use permitted Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms. | Qwen3.8-Max |
Qwen Community License 1.0 | Commercial use permitted Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms. | Qwen3.8-Flash-Next |
| Qwen3.8 27B | Min 18 GB VRAM · Q4_K_M · 262,144 ctx · ollama run qwen3.8:27b |
| Qwen3.8-Max | Min 1450 GB VRAM · Q4_K_M · 262,144 ctx · |
| Qwen3.8-Flash-Next | Min 109 GB VRAM · Q4_K_M · 262,144 ctx · |
The cheapest GPU that runs Qwen3.8 locally (min 18 GB VRAM) is the AMD Radeon RX 7900 XT (20 GB).
Install Ollama then run: ollama run qwen3.8:27b
Minimum VRAM: 18 GB. For best results use Q4_K_M quantization.
Qwen3.8 needs about 18 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Qwen3.8 27B (18 GB, Q4_K_M); Qwen3.8-Max (1450 GB, Q4_K_M); Qwen3.8-Flash-Next (109 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Qwen3.8 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 Qwen3.8 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 qwen3.8:27b. This downloads Qwen3.8 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.