Ollama, a framework for managing large language models locally, is experiencing significant community interest but grapples with performance issues related to model loading and GPU utilization.
Recent issues and pull requests (PRs) highlight ongoing challenges with model performance, particularly concerning GPU utilization and memory management. Users have reported problems such as the inability to fully utilize VRAM (#6456) and network errors during model access. The development team is actively addressing these concerns through various PRs aimed at enhancing CUDA support (#6455) and improving memory management (#6467).
Daniel Hiltgen (dhiltgen)
Michael Yang (mxyng)
Jeffrey Morgan (jmorganca)
Roy Han (royjhan)
Josh (joshyan1)
Blake Mizerany (bmizerany)
The team shows strong collaboration, particularly among key contributors like Daniel Hiltgen and Michael Yang. There is a clear focus on performance optimization and user experience improvements, with active efforts to address technical challenges such as memory management and compatibility with OpenAI APIs.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 71 | 36 | 268 | 2 | 1 |
| 14 Days | 156 | 91 | 619 | 5 | 1 |
| 30 Days | 393 | 193 | 1690 | 10 | 1 |
| All Time | 4081 | 3048 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Daniel Hiltgen |  |
4 | 30/22/2 | 49 | 277 | 107717 |
| Josh |  |
8 | 10/4/1 | 46 | 33 | 4016 |
| royjhan |  |
6 | 11/8/2 | 35 | 24 | 2658 |
| Patrick Devine (pdevine) | 2 | 2/0/0 | 23 | 20 | 1459 | |
| Michael Yang |  |
4 | 17/13/1 | 24 | 90 | 1454 |
| Jeffrey Morgan |  |
5 | 12/11/0 | 29 | 28 | 1446 |
| Jesse Gross |  |
2 | 3/3/0 | 18 | 13 | 427 |
| Blake Mizerany |  |
3 | 4/4/0 | 7 | 5 | 292 |
| Bruce MacDonald |  |
2 | 1/1/0 | 2 | 3 | 110 |
| Kim Hallberg |  |
1 | 2/2/0 | 2 | 20 | 58 |
| slouffka |  |
1 | 1/1/0 | 5 | 1 | 55 |
| Michael |  |
1 | 2/2/0 | 2 | 1 | 32 |
| longtao |  |
1 | 3/2/1 | 2 | 6 | 31 |
| Richard Lyons |  |
1 | 0/0/0 | 3 | 1 | 9 |
| Nicholas Schwab |  |
1 | 0/0/0 | 2 | 1 | 8 |
| Tibor Schmidt |  |
1 | 0/0/0 | 1 | 6 | 6 |
| Kyle Kelley |  |
1 | 1/1/0 | 1 | 1 | 4 |
| Weiwei |  |
1 | 1/1/0 | 1 | 1 | 3 |
| Chua Chee Seng |  |
1 | 1/1/0 | 1 | 1 | 2 |
| Veit Heller |  |
1 | 1/1/0 | 1 | 1 | 2 |
| Ikko Eltociear Ashimine |  |
1 | 1/1/0 | 1 | 1 | 2 |
| Lei Jitang |  |
1 | 1/1/0 | 1 | 1 | 2 |
| frob |  |
1 | 2/2/0 | 1 | 1 | 2 |
| Ivan Charapanau |  |
1 | 1/1/0 | 1 | 1 | 1 |
| Ajay Chintala |  |
1 | 0/1/0 | 1 | 1 | 1 |
| sryu1 |  |
1 | 1/1/0 | 1 | 1 | 1 |
| Pamela Fox |  |
1 | 1/1/0 | 1 | 1 | 1 |
| Nicholas42 |  |
1 | 2/1/0 | 1 | 1 | 1 |
| Daniel Nguyen |  |
1 | 1/1/0 | 1 | 1 | 1 |
| CognitiveTech |  |
1 | 1/1/0 | 1 | 1 | 1 |
| Vishal Rao (vjr) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Russell Smith (ukd1) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Ramiro Gómez (yaph) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Michael (bean5) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Mitar (mitar) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (Binozo) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (JHubi1) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Sam (sammcj) | 0 | 5/0/4 | 0 | 0 | 0 | |
| Christian Tzolov (tzolov) | 0 | 1/0/0 | 0 | 0 | 0 | |
| ethan (farwish) | 0 | 1/0/0 | 0 | 0 | 0 | |
| sudo pacman -Syu (haunt98) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Nikita Lukianets (nikiluk) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Yevhen Vitruk (vertrue) | 0 | 1/0/2 | 0 | 0 | 0 | |
| chen (wszgrcy) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Thomas Lavoie (Calvicii) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Jens Rapp (TecDroiD) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Erkin Alp Güney (erkinalp) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Evshiron Magicka (evshiron) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (jing-rui) | 0 | 1/0/0 | 0 | 0 | 0 | |
| kallados (kallados) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Lukas Prediger (lupreCSC) | 0 | 1/0/0 | 0 | 0 | 0 | |
| venjiang (venjiang) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Rune Berg (1runeberg) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Arda Günsüren (ArdaGnsrn) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Carter (Carter907) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Piet Jarmatz (Thinkpiet) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Akash Patel (akashaero) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (albertotn) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Deep Lakhani (deep93333) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Aarushi (aarushik93) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Bryan Honof (bryanhonof) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Emir Sahin (emirsahin1) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Lennart J. Kurzweg (noggynoggy) | 0 | 1/0/0 | 0 | 0 | 0 | |
| zhong (zhongTao99) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Gabe Goodhart (gabe-l-hart) | 0 | 2/0/0 | 0 | 0 | 0 | |
| Hernan Martinez (hmartinez82) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Teïlo M (teilomillet) | 0 | 1/0/0 | 0 | 0 | 0 | |
| 苏业钦 (HougeLangley) | 0 | 1/0/0 | 0 | 0 | 0 | |
| digua (Potato-DiGua) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Tomoya Fujita (fujitatomoya) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Igor Drozdov (igor-drozdov) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Kemal Elmizan (kemalelmizan) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Ricky Bobby (rpreslar4765) | 0 | 1/0/1 | 0 | 0 | 0 | |
| None (wallacelance) | 0 | 1/0/0 | 0 | 0 | 0 | |
| 王卿 (wangqingfree) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Amith Koujalgi (amithkoujalgi) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (MaciejMogilany) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Vaibhav Acharya (VaibhavAcharya) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Kevin Thomas (mytechnotalent) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Sergey K (sergeykorablin) | 0 | 1/0/0 | 0 | 0 | 0 | |
| ZhangYunHao (zhangyunhao116) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (lorenzodimauro97weplus) | 0 | 1/0/0 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
The Ollama project has seen significant recent activity, with 1033 open issues currently logged. The most pressing concerns revolve around bugs related to model loading, performance issues with GPU utilization, and feature requests for improved model management and integration capabilities. A notable trend is the increasing number of users reporting problems with specific models, particularly regarding their ability to handle large inputs or maintain performance under load.
Several issues highlight recurring themes, such as difficulties in accessing models due to network errors (e.g., TLS handshake timeouts) and inconsistencies in GPU usage when running different models. Additionally, there are numerous requests for new features and enhancements, indicating a vibrant community eager for improvements.
Issue #6468: bug: Nested model in registry - cannot access model settings on my own model at ollama.com
Issue #6466: I can not push 8g model to Ollama
Issue #6464: Error: unsupported content type: unknown
Issue #6460: glm-4v-9b
Issue #6457: Request official guidelines
Issue #6456: Ollama not using 20GB of VRAM from Tesla P40 card
Issue #6454: obtain attention matrices during inference, similar to the output_attentions=True parameter in the transformers package
Issue #6456 (Edited):
Issue #6449 (Edited):
Issue #6448 (Edited):
Issue #6447 (Edited):
Issue #6446 (Edited):
Several issues indicate a pattern of users encountering problems with specific models, particularly around memory allocation and GPU utilization. For example, users have reported that the Ollama framework fails to utilize available VRAM effectively, leading to performance bottlenecks when running larger models like Llama3.1 or Mistral Nemo.
Additionally, network-related issues such as TLS handshake timeouts have been a common theme among users attempting to pull models from the registry, suggesting potential infrastructure challenges or misconfigurations affecting accessibility.
The presence of multiple feature requests indicates a strong demand for enhancements in usability and functionality, particularly regarding model management and integration capabilities with existing tools and workflows.
Overall, while Ollama has garnered significant community interest and contributions, it faces challenges related to stability and performance that need addressing to maintain user satisfaction and engagement.
The analysis of the pull requests (PRs) for the Ollama project reveals a vibrant and active development environment, with a total of 282 open PRs and 1,918 closed PRs. The recent PRs focus on enhancing community integrations, improving documentation, and addressing technical issues related to model performance and compatibility.
The current landscape of pull requests in the Ollama project indicates a strong emphasis on community engagement and integration capabilities. The addition of various community integrations, such as the 'Ollama App' (#6465) and AutoGPT (#6459), highlights an ongoing effort to enhance the usability and accessibility of Ollama's features across different platforms.
Notably, many of the recent PRs focus on improving documentation and user guidance, such as #6450 and #6445, which aim to clarify installation instructions and enhance user experience. This trend suggests that the maintainers are keenly aware of the importance of clear communication in fostering a supportive community around the project.
Technical improvements are also prevalent, with PRs addressing specific issues like CUDA configurations (#6455), function calling enhancements (#6452), and memory management optimizations (#6467). These efforts reflect a commitment to maintaining high performance and reliability within the framework, which is crucial given the complexity associated with managing large language models.
However, there are indications of potential challenges as well. The number of open PRs (282) alongside a significant volume of closed ones (1,918) may suggest that while contributions are being made, there might be bottlenecks in review processes or resource allocation for merging these contributions into the main codebase. Additionally, some discussions within PR comments indicate ongoing technical disputes or uncertainties regarding implementation details (e.g., #6181).
Overall, Ollama's pull request activity showcases a dynamic development environment characterized by active community involvement, continuous improvement efforts, and a focus on enhancing both functionality and user experience. However, it also points to potential areas for improvement in managing contributions effectively to maintain momentum in project development.
Daniel Hiltgen (dhiltgen)
Michael Yang (mxyng)
Jeffrey Morgan (jmorganca)
Roy Han (royjhan)
Josh (joshyan1)
Blake Mizerany (bmizerany)
Others (e.g., frob-cloudstaff, zwwhdls, etc.)
Collaboration: There is a strong collaborative effort among team members, particularly between Daniel Hiltgen, Michael Yang, and Jeffrey Morgan, who frequently work together on pull requests that involve complex features like CUDA support and model conversion enhancements.
Focus Areas: The recent activities indicate a concentrated effort on improving memory management, enhancing model support, refining CI/CD processes, and ensuring compatibility with OpenAI APIs. This suggests that the team is prioritizing both performance optimizations and user experience improvements.
Active Development: The high number of commits across various branches indicates that the project is actively being developed with ongoing feature additions, bug fixes, and optimizations. The presence of numerous open pull requests also reflects a vibrant development environment where contributions are continuously integrated.
Testing and Reliability: There is a notable emphasis on improving testing frameworks and addressing race conditions, which highlights the team's commitment to delivering a stable product.
Overall, the development team appears to be well-coordinated with clear objectives focused on enhancing the Ollama framework's capabilities while ensuring robust performance across various platforms.
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