Qwen

@Alibaba_Qwen

Open foundation models for AGI.

qwen.ai

Joined February 2024

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Qwen
@Alibaba_Qwen
16h
⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram
Qwen
@Alibaba_Qwen
1h
Fantastic! 🥳 Thanks @lightseekorg for the TokenSpeed day-0 support. The new architecture is fully covered, from GDN + QSA to N-gram embedding with FP8.
LightSeek Foundation
@lightseekorg
14h
TokenSpeed Day 0 Support for @Alibaba_Qwen 3.8 Flash Next. 🔹GDN + Qwen Sparse Attention hybrid architecture 🔹Gated residual connections 🔹N-gram embedding For N-gram embedding, TokenSpeed also supports FP8 precision. As a preview for Qwen 4, we will continue optimizing
Qwen
@Alibaba_Qwen
2h
Thank you @NVIDIAAI for the day-0 support! 🙌 Developers can finetune the model for domain-specific use cases using NVIDIA NeMo AutoModel: github.com/NVIDIA-NeMo/Au…
NVIDIA AI
@NVIDIAAI
12h
Congrats to @Alibaba_Qwen on releasing Qwen3.8-Flash-Next, an experimental open-weight model that previews the Qwen4 architecture. We’ve got Day 0 support to fine-tune with NVIDIA NeMo AutoModel and NeMo RL, plus recipes to run it with @sgl_project, @vllm_project and
Qwen
@Alibaba_Qwen
13h
A high-performance 125B model now running locally on just 75GB RAM! Thank you @UnslothAI for the day-0 support.🥳
Unsloth AI
@UnslothAI
13h
Qwen3.8-Flash can now be run locally! 🔥 The 125B MoE model outperforms Claude-Opus-4.6 (Max). Run on 75GB RAM via Unsloth GGUFs. Qwen3.8-Flash-Next enables CPU RAM / unified mem setups to deliver near VRAM speeds. Guide: unsloth.ai/docs/models/qw… GGUF: huggingface.co/unsloth/Qwen3.…
Qwen
@Alibaba_Qwen
13h
API is live on QwenCloud: qwencloud.com/models/qwen3.8… 🙌Let's build something!
Qwen
@Alibaba_Qwen
16h
⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram