---
格式版本: 2
标题: "Compatible Alibaba Models"
原文链接: "https://docs.oracle.com/iaas/Content/generative-ai/imported-alibaba-models.htm"
发布日期: "2026-09-04"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "rule:local:strict_original_body"
发布时间证据: "Updated 2026-09-04"
发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body"
发布时间校准置信度: "high"
发布时间候选数量: 8
发布时间严格候选数量: 1
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-09-06T18:20:03+08:00"
发布时间仲裁状态: "skipped"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-09-06T17:43:35+08:00"
入库时间: "2026-09-06T10:26:21.000Z"
来源平台: "固定入口"
搜索渠道: "fixed_url"
搜索词: "https://docs.oracle.com/en-us/iaas/releasenotes"
匹配关键词:
  - "AI"
相关厂家:
  - "Oracle"
  - "OpenAI"
相关专家:
  []
内容类型: "网页"
抓取工具: "Jina Reader"
清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 26
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "正文主线为Oracle OCI Generative AI对通义千问系列模型的导入兼容性说明，属于模型服务文档，并非rack-scale AI基础设施或超节点技术；虽然列出Qwen3.8-2.4T-A95B-FP8需要B200_X16的Dedicated AI Cluster硬件形状，但无超节点架构、互连、拓扑、供电、液冷或量产部署信息，属于模型层兼容列表而非机架级基础设施。来源为Oracle官方文档，权威性可给分；历史对照知识库可见OCI在NVIDIA GB300/HGX B300和Vera等基础设施进展，但本文仅补充模型导入兼容和计费规则，无可核验的机架级新增事实。未命中任何高价值准入通道，且命中断言否决中的服务文档/模型应用范畴，故判定非优质。"
AI质检模型: "zj-deepseek-v4-flash"
AI质检时间: "2026-09-06T18:26:31+08:00"
AI主题相关性: 1
AI来源权威性: 10
AI新颖性: 3
AI技术细节: 2
AI商业部署信号: 4
AI完整性: 6
AI评分提示词版本: "v17-精简生产版"
AI评分提示词SHA256: "48fb9777f386026761b4873eaff30807694fb11e9b352d7c69bf2dfde750cc7d"
AI评分知识库版本: "knowledge_base_v1-20260819+runtime.96"
AI评分知识库SHA256: "0cae74ad2e30b227036c5952745542146844722958bc01868bbd0799ab59eeec"
AI评分知识库检索词: "[\"Oracle\",\"https://docs.oracle.com/en-us/iaas/releasenotes\",\"RAS\",\"Meta\",\"Qwen/Qwen3.8-2.4T-A95B-FP8\",\"URL\",\"HTTP\",\"UK\",\"EU\",\"OCI\",\"OHPPanel1nav0625\",\"qwen-3-next\"]"
AI评分知识库命中: "[{\"id\":\"runtime-dccd09f98e2ca028070793f0\",\"title\":\"https://blogs.oracle.com/cloud-infrastructure/oci-achieves-nvidia-exemplar-cloud\",\"sourceType\":\"ai_excellent_article\",\"time\":\"\",\"matchedTerms\":[\"Oracle\",\"RAS\",\"HTTP\",\"OCI\"],\"rank\":-12.868095013011326},{\"id\":\"july-correct-0112\",\"title\":\"超前点映AMD Advancing AI 2026：AMD AI YES？-36氪\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"Meta\",\"URL\",\"HTTP\",\"UK\",\"EU\"],\"rank\":-11.390485254684464},{\"id\":\"runtime-66d1ddc58948150bffee973a\",\"title\":\"Delivering Vera: NVIDIA’s First CPU Built for Agents Is Shipping Now\",\"sourceType\":\"ai_excellent_article\",\"time\":\"\",\"matchedTerms\":[\"Oracle\",\"RAS\",\"HTTP\",\"EU\",\"OCI\"],\"rank\":-11.343950238269914},{\"id\":\"july-correct-0015\",\"title\":\"AMD to join the optical interconnect party with 2027 Instinct GPUs\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"Meta\",\"URL\",\"HTTP\",\"OCI\"],\"rank\":-10.883295192884612},{\"id\":\"runtime-0210d17c29c694da454d0e78\",\"title\":\"HPE caps stellar Q3 with Oracle ‘gigawatt scale’ networking deal\",\"sourceType\":\"ai_excellent_article\",\"time\":\"\",\"matchedTerms\":[\"Oracle\",\"RAS\",\"HTTP\",\"OCI\"],\"rank\":-10.069395692560379}]"
采集批次: "2026年9月6日17点41分46秒"
采集批次ID: "20260906-174146-0dbc83f7"
去重键: "https://docs.oracle.com/iaas/Content/generative-ai/imported-alibaba-models.htm"
---

Title: Compatible Alibaba Models

URL Source: https://docs.oracle.com/iaas/Content/generative-ai/imported-alibaba-models.htm

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*   [Qwen 2](https://docs.oracle.com/iaas/Content/generative-ai/imported-alibaba-models.htm#qwen-2)

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Updated 2026-09-04

[Skip to main content](https://docs.oracle.com/iaas/Content/generative-ai/imported-alibaba-models.htm#dcoc-content-body)

Updated 2026-09-04

# Compatible Alibaba Models

You can import large language models from Hugging Face and OCI Object Storage buckets into OCI Generative AI, create endpoints for those models, and use them in the Generative AI service.

**Note**

Before selecting a model, review the guidance following the model tables on this page for selecting a compatible hardware unit shape and understanding billing for dedicated AI clusters that host imported models.

Alibaba Qwen model family, feature advanced multilingual and multimodal capabilities. For model cards on Hugging Face, see the links in the following tables.

## Qwen 3 Next🔗

Compatible Qwen 3 Next Model| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) | TEXT_TO_TEXT | * A100_80G_X4 * H100_X4 * H200_X4 |

## Qwen 3.8🔗

Compatible Qwen 3.8 Model| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen3.8-2.4T-A95B-FP8](https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B-FP8) | TEXT_TO_TEXT | * B200_X16 |

## Qwen 3.6🔗

Compatible Qwen 3.6 Model| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) | IMAGE_TEXT_TO_TEXT | * A100_80GB_X2 * H100_X2 * H200_X1 * B200_X1 |

## Qwen 3.5🔗

Compatible Qwen 3.5 Model| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B) | IMAGE_TEXT_TO_TEXT | * H200_X8 * B300_X4 |
| [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) | IMAGE_TEXT_TO_TEXT | * A100_80GB_X1 * H100_X1 * H200_X1 * B200_X1 |

## Qwen Image🔗

Compatible Qwen Image Models| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image) | TEXT_TO_IMAGE | A100_80G_X1 |
| [Qwen/Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit) | IMAGE_TEXT_TO_IMAGE | A100_80G_X1 |
| [Qwen/Qwen-Image-2512](https://huggingface.co/Qwen/Qwen-Image-2512) | TEXT_TO_IMAGE | A100_80G_X1 |
| [Qwen/Qwen-Image-Edit-2511](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) | IMAGE_TEXT_TO_IMAGE | A100_80G_X1 |
| [Qwen/Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) | IMAGE_TEXT_TO_IMAGE | A100_80G_X1 |

**Note**

*   `response_format: "url"` doesn't work and returns an HTTP 400 bad request error.
*   `n` (number of images): only `0` or `1` work.
*   Streaming isn’t compatible.
*   Non-standard image sizes might be rounded (for example, `999x999` → `992x992`) instead of returning an HTTP 400 (unlike the OpenAI API).
*   Transparency might not work because of model limitations.

## Qwen Q (Reasoning)🔗

Compatible Qwen Q Models| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | TEXT_TO_TEXT | A100_80G_X2 |

## Qwen 3🔗

Compatible Qwen 3 Models| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) | EMBEDDING | A10_X1 |
| [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) | EMBEDDING | A10_X2 |
| [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | EMBEDDING | A100_80G_X1 |
| [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | TEXT_TO_TEXT | A100_80G_X2 |
| [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) | TEXT_TO_TEXT | A100_80G_X2 |
| [Qwen/Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) | TEXT_TO_TEXT | H100_X8 |
| [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct) | IMAGE_TEXT_TO_TEXT | H100_X2 |
| [Qwen/Qwen3-VL-30B-A3B-Instruct-FP8](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-FP8) | IMAGE_TEXT_TO_TEXT | * H100_X2 * H200_X2 |
| [Qwen/Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) | IMAGE_TEXT_TO_TEXT | H100_X8 |

## Qwen 2.5🔗

Compatible Qwen2.5 Models| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) | TEXT_TO_TEXT | A100_80G_X2 |
| [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | TEXT_TO_TEXT | A100_80G_X2 |
| [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | TEXT_TO_TEXT | A100_80G_X4 |
| [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X2 |
| [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X4 |

## Qwen 2🔗

Compatible Qwen2 Models| Hugging Face Model ID | Model Capability | Minimum Dedicated AI Cluster Unit Shape |
| --- | --- | --- |
| [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) | TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) | TEXT_TO_TEXT | A100_80G_X4 |
| [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X1 |
| [Qwen/Qwen2-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct) | IMAGE_TEXT_TO_TEXT | A100_80G_X4 |

**Important**

*   For imported models, you can use the native context length specified by the model provider. However, the effective maximum context length is limited by the underlying hardware setup that you select for the hosting dedicated AI clusters in OCI Generative AI. To take full advantage of a model's native context length, you might need to provision more hardware resources.
*   Use the fine-tuned models only if they match the compatible base model's transformer version and have a parameter count within ±10% of the original.
*   To decide where you can host an imported model, find a compatible unit shape for the model in the table on this page, and then confirm that the hardware shape is available in the target region in [Hardware Unit Shapes by Region](https://docs.oracle.com/iaas/Content/generative-ai/hardware-unit-shapes-by-region.htm#imported-model-shapes). A model can be hosted in a region only when the region provides a hardware shape listed for that model.
*   For steps on how to deploy the imported models, see [Managing Imported Models](https://docs.oracle.com/iaas/Content/generative-ai/manage-imported-models.htm#imported-models "In addition to using the hosted pretrained models in OCI Generative AI, you can import validated open source and third-party models (for example, from Hugging Face) into OCI Generative AI, host them, create endpoints, and use them like any other model.").

**Note**

**Billing for Dedicated AI Clusters Hosting Imported Models**

Dedicated AI clusters that host **imported models** don't require the 744-unit-hour minimum commitment that applies to dedicated AI clusters hosting pretrained models available in OCI Generative AI. Instead, an imported-model dedicated AI cluster has a minimum billable duration of one hour. For example, a cluster that uses one AI unit and runs for 30 minutes is billed for one AI unit-hour.

After the first hour, charges are based on the cluster's actual running time and are prorated to the millisecond. For example, a cluster that runs for one hour and one minute is billed for that duration, rather than for two full hours.

OCI sends usage records to Metering and Billing every five minutes while the cluster is running. If the cluster is deleted before the one-hour minimum is reached, OCI continues reporting billable usage until the minimum is met. If the cluster runs for more than one hour, OCI continues sending usage records every five minutes until the cluster is deleted.

Was this article helpful?

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