Qwen3.8-Flash-Next SGLang Deployment
Qwen4's 51B N-gram embeddings scale model capacity with minimal extra compute per token, making it efficient for long-horizon tasks in the region.
Alibaba_Qwen launched Qwen3.8-Flash-Next with SGLang support on day 0. The Qwen4 architecture preview features a 125B main model with 51B N-gram embeddings and 6B activated per token. The model uses GDN + QSA hybrid attention for efficient memory and precise retrieval on long-horizon tasks. SGLang supports the new architecture with its deployment framework.
Big thanks to @sgl_project for the day-0 support! 🙌 Qwen3.8-Flash-Next is ready to deploy with SGLang today. SGLang @sgl_project Congrats to @Alibaba_Qwen on launching Qwen3.8-Flash! SGLang is proud to be a day-0 partner supporting the new architecture preview for Qwen4. It's a 125B main model with 51B of N-gram embeddings and 6B activated per token. The 51B N-gram embeddings scale model capacity with almost no extra compute per token, and can sit in host memory with async prefetch instead of occupying GPU memory. The GDN + QSA hybrid attention gives you efficient memory and precise retrieval at the same time on long-horizon tasks, while Gated Residual gives the model 4 lanes instead of 1 to pass information between layers. And it's trained with Muon! We're excited for what's next with Qwen4, and we already have plenty of ideas for how to use the N-gram embeddings in new deployment setups. Stay tuned! Blog and cookbook in the comments👇 🔗 View Quoted Tweet 💬 7 🔄 2 ❤️ 120 👀 11800 📊 13 ⚡
