langgenius/dify
1
0
Fork 0
mirror of https://github.com/langgenius/dify.git synced 2026-07-21 20:15:31 -04:00
Code Issues 775 Packages Projects Releases 10 Wiki Activity

Will different context length parameters in large language models cause Ollama to create and load multiple model instances when performing Q&A? #12115

New Issue
Closed
opened 2026-02-21 19:05:51 -05:00 by yindo · 2 comments
yindo commented 2026-02-21 19:05:51 -05:00
Owner
Copy Link
Copy Source

Originally created by @jaybom on GitHub (Mar 25, 2025).

Self Checks

  • This is only for bug report, if you would like to ask a question, please head to Discussions.
  • I have searched for existing issues search for existing issues, including closed ones.
  • I confirm that I am using English to submit this report (我已阅读并同意 Language Policy).
  • [FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:)
  • Please do not modify this template :) and fill in all the required fields.

Dify version

1.1.0

Cloud or Self Hosted

Self Hosted (Docker)

Steps to reproduce

In orchestration, using the same model with different context length parameters for Q&A can cause a new large model instance to be loaded into GPU memory. If GPU memory is insufficient, this can force the large model to be offloaded to CPU, which may potentially lead to server crashes.

✔️ Expected Behavior

How can we prevent creating a new complete model instance in GPU when using different context lengths?

❌ Actual Behavior

No response

Originally created by @jaybom on GitHub (Mar 25, 2025). ### Self Checks - [x] This is only for bug report, if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general). - [x] I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones. - [x] I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)). - [x] [FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:) - [x] Please do not modify this template :) and fill in all the required fields. ### Dify version 1.1.0 ### Cloud or Self Hosted Self Hosted (Docker) ### Steps to reproduce In orchestration, using the same model with different context length parameters for Q&A can cause a new large model instance to be loaded into GPU memory. If GPU memory is insufficient, this can force the large model to be offloaded to CPU, which may potentially lead to server crashes. ### ✔️ Expected Behavior How can we prevent creating a new complete model instance in GPU when using different context lengths? ### ❌ Actual Behavior _No response_
yindo added the 🙋‍♂️ question label 2026-02-21 19:05:51 -05:00
yindo closed this issue 2026-02-21 19:05:51 -05:00
yindo commented 2026-02-21 19:05:51 -05:00
Author
Owner
Copy Link
Copy Source

@dosubot[bot] commented on GitHub (Mar 25, 2025):

Dify handles different context length parameters by adjusting the max_tokens dynamically to fit within the model's token limit, rather than creating new model instances for each context length. This approach helps manage the context size without loading multiple instances into GPU memory [1]. However, if you're experiencing issues with GPU memory when using different context lengths, it might be due to other factors not directly related to Dify's handling of context lengths. Unfortunately, I couldn't find specific information on how Dify manages GPU memory allocation or prevents loading multiple model instances in such scenarios.

To continue talking to Dosu, mention @dosu.

Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other

@dosubot[bot] commented on GitHub (Mar 25, 2025): <!-- Answer --> Dify handles different context length parameters by adjusting the `max_tokens` dynamically to fit within the model's token limit, rather than creating new model instances for each context length. This approach helps manage the context size without loading multiple instances into GPU memory <sup>[[1]](https://github.com/langgenius/dify/blob/main/api/core/app/apps/base_app_runner.py)</sup>. However, if you're experiencing issues with GPU memory when using different context lengths, it might be due to other factors not directly related to Dify's handling of context lengths. Unfortunately, I couldn't find specific information on how Dify manages GPU memory allocation or prevents loading multiple model instances in such scenarios. <!-- Dosu Comment Footer --> *To continue talking to [Dosu](https://dosu.dev), mention @dosu.* --- <sup>Help Dosu learn! Give it feedback: [Great Response](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=great_response) | [Irrelevant Answer](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=irrelevant_answer) | [Incorrect Sources](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=incorrect_sources) | [Too Verbose](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=too_verbose) | [Hallucination](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=hallucination) | [Bug Report](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=bug_report) | [Other](https://app.dosu.dev/response-feedback/1ef7f3d2-c19e-4d8b-b357-af6fb5e69925?feedback_type=other)</sup>
yindo commented 2026-02-21 19:05:51 -05:00
Author
Owner
Copy Link
Copy Source

@crazywoola commented on GitHub (Mar 25, 2025):

This is only for bug report, if you would like to ask a question, please head to Discussions.

@crazywoola commented on GitHub (Mar 25, 2025): This is only for bug report, if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general).
Sign in to join this conversation.
No Branch/Tag Specified
Branches Tags
Labels
Clear labels
Knowledge Pipeline
Proposal
WIP
cloud
dependencies
feat:webapp
good first issue
outdated
performance
pull-request
Mirrored from GitHub Pull Request
 python:uv
refactor
size:L
stale
status: accepting prs
status: blocked
status: in discussion
stt & tts
vulnerablity
web
wont-fix
🌊 feat:workflow
🌚 invalid
🐞 bug
👻 feat:rag
💪 enhancement
📚 documentation
🔨 feat:tools
🙋‍♂️ question
🙏 help wanted
🤔 cant-reproduce
🤖 feat:agent
No Label 🙋‍♂️ question
Milestone
No items
No Milestone
Projects
Clear projects
No project
Assignees
Clear assignees
yindo
No Assignees
1 Participants
Notifications
Due Date
No due date set.
Dependencies

No dependencies set.

Reference: langgenius/dify#12115
Delete Branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?