Alibaba just made good on a promise. Qwen3.8 open weights are now live on Hugging Face and ModelScope, delivering two very different models for very different hardware budgets: the compact Qwen3.8-27B, a deployment-friendly dense model, and the flagship Qwen3.8-2.4T-A95B, a massive mixture-of-experts (MoE) model that is the first Max-class Qwen release ever made downloadable. Both ship under Apache 2.0.

Two models, one announcement

Alibaba released Qwen3.8-Max as a 2.4-trillion-parameter sparse MoE model with 95 billion active parameters per token. MoE means the model has a huge number of parameters total, but only a fraction of them activate on any given token, keeping inference costs manageable relative to the raw parameter count. This is the first time Qwen is opening the weights of a Max-class model.

Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability.

The architecture under the hood

Qwen3.8-27B is a dense model, meaning every parameter activates on every token. That makes VRAM usage predictable and latency low, which is exactly what you want for local deployment. But fitting a 262K-token context into a dense 27B model without blowing up memory requires a clever architectural trick.

Qwen3.8-27B is organized as 64 layers, structured as 16 repeating blocks, each composed of three Gated DeltaNet sublayers followed by a single Gated Attention sublayer, with feed-forward networks interleaved throughout. Three-quarters of the model's attention budget runs on linear attention; only every fourth sublayer performs full self-attention. Gated DeltaNet is a form of linear attention: instead of computing relationships between every pair of tokens (which scales as O(n²) and becomes ruinously expensive at 262K tokens), it trades a small amount of expressive capacity for O(n) complexity. Full Gated Attention appears only where it earns its keep , roughly once per block , to preserve the long-range precision that linear attention alone cannot.