FlagRelease/Qwen3.8-27B-BF16-kunlunxin-FlagOS

VISIONPricing:Input $1.6 / Cached $0.15 / Output $12Concurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

FlagRelease/Qwen3.8-27B-BF16-kunlunxin-FlagOS is a 27 billion parameter vision-language model, derived from Alibaba's Qwen3.8-27B, specifically adapted by the Zhongzhi (众智) FlagOS community for Kunlunxin AI chips. This model is optimized for multi-chip deployment and precision alignment across 11 different AI accelerators, with this version running on BF16 precision. It provides out-of-the-box inference solutions, focusing on broad hardware compatibility and efficient deployment for AI workloads.

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Overview

FlagRelease/Qwen3.8-27B-BF16-kunlunxin-FlagOS is a 27 billion parameter vision-language model, adapted from Alibaba's Qwen3.8-27B by the Zhongzhi (众智) FlagOS community. This specific release is optimized for deployment on Kunlunxin AI chips, running with BF16 precision. It is part of the FlagOS initiative to provide a unified open-source technology stack for multi-chip adaptation, enabling a "develop once, run anywhere" workflow across diverse AI accelerators.

Key Capabilities & Features

  • Multi-Chip Adaptation: Successfully adapted and verified across 11 different AI chips, including Kunlunxin, NVIDIA, and Ascend.
  • Precision Alignment: Supports BF16 precision deployment on most adapted chips, with FP8 support for NVIDIA and Moore Threads.
  • Integrated Deployment: Offers out-of-the-box inference scripts and a dedicated FlagOS-Kunlunxin container image for rapid setup.
  • Consistency Validation: Rigorously evaluated through benchmark testing to ensure performance and results align with native stacks.
  • FlagOS Ecosystem: Leverages core FlagOS technologies like FlagScale, FlagGems (high-performance operator library), FlagCX (communication library), and FlagTree (unified compiler) for efficient model migration and deployment.

Use Cases

This model is ideal for developers and organizations seeking to deploy large language models on diverse hardware platforms, particularly those utilizing Kunlunxin AI chips. Its primary strength lies in providing a pre-adapted, validated solution that simplifies the deployment and maintenance of AI workloads across various accelerators, reducing fragmentation and porting costs.

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