HAMi Adopters

Organizations below all are using HAMi in production.

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HAMi Adopters

All organizations are sorted alphabetically by the first letter of their English names.

Organization	Contact	Environment	Description of Use
4paradigm	@archlitchi	Production	Device sharing for third-party hardware (GPU, NPU, MLU, etc.)
Beijing Chenan	@Chenyangzh	Production	Deep learning algorithm inference.
Beijing Unit Technology Co., Ltd.	@jingzhe6414	Production	AI computing platform, refined resource allocation.
Caper	@summerisc	Production	Physical GPU partitioning, used with Volcano scheduler for automatic training pipelines.
Chengqi Technology	@x1y2z3456	Testing	Effective GPU isolation.
Chengyu Wisdom	@x1y2z3456	Production	Deep learning training, educational and research institutions.
China East Telecom	@fangfenghuang	Testing	GPU virtualization to solve GPU resource sharing problems.
China Mobile	@ssslkj123	Staging/Production	GPU resource pooling, tenant isolation based on GPU time slicing and memory quota control, machine learning operations, and sales scenarios; offline deployment of helm templates is not user-friendly and deployment is complex.
China Unicom Industrial Internet	@zqz199	Evaluation/Testing	Attempting to build a systematic platform for ai training and inference that can allocate GPU resources with fine-grained partitioning.
China University of Mining and Technology	@hyc-yuchen	Evaluation	Perform GPU virtualization and use k8s to schedule GPU resources.
Chongyue Computer Network Technology Co., Ltd.	@stormdragongardin	Evaluation/Testing	Cloud platform development.
DaoCloud	@wawa0210	Production	Used for our cloud-native AI products.
Donghua University	@kirakiseki	Evaluation/Testing	Use this plug-in to run high video memory demand tasks, use k8s to schedule GPU resources, and provide flexible resource allocation support for learning and training scenarios.
Gsafety	@liuchunhui-c	Production	3 nodes reasoning training.
Guangdong University of Technology	@cccusername	Evaluation/Testing	research on GPU virtualization technology and GPU isolation
Guangzhou Pingao	@zhangQiWorr	Evaluation	GPU heterogeneous resource scheduling research.
Haofang	@khw934	Evaluation	Testing various GPU virtualization scenarios to fully utilize GPU resources; requested support for using cpu resources to replace GPU computing power; requested support for the function of consolidating fragmented resources (e.g., if one card has 0.3 remaining and another has 0.5, it should be possible to apply for 0.7).
Hangzhou Lianhui	@louyifei8888, @xyy1999	Evaluation/Testing	GPU usage isolation, research on maximizing GPU resource utilization.
Harbin Institute of Technology	@blackjack2015	Production	GPU cluster management of the research group.
H3C	@chenxj1997	Testing	Implemented GPU isolation.
Huawei	@AlexPei	Evaluation	Testing resource isolation for multiple deep learning inference services (multi-container) sharing a single card; found that with continuously increasing concurrent requests, video memory continues to increase and does not release after stopping the stress test; the utilization rate of GPU computing units exceeds the set value.
iFlytek	@whybeyoung	Production	Public cloud reasoning, training.
Infervision	@freemanke	Evaluation	Model inference.
Kylinsoft	cuiyudong-free	Testing/Staging	Deploy HAMi functions in AI scenarios of cloud- or server-based operating system.
Linklogis	@rnyrnyrny	Production	Online inference service.
Miaoyun	@erganzi	Evaluation	Perform GPU virtualization and use k8s to schedule GPU resources.
Nankai University - Network Laboratory	@liudsl	Evaluation	Preliminary research on GPU computing resource allocation and isolation for scheduling algorithm research.
Ping An Bank	@jamie-liu	Testing	Solved the problem of insufficient GPU resources and improved resource utilization.
Ping An Securities	@detongz	Testing/Staging	Used with Kubeflow to allocate a single GPU to multiple notebooks, improving work efficiency; occasionally encountered jupyter kernel crashes (later resolved by adjusting parameters).
ppio/	@zeta65	Evaluation	AI computing to improve resource utilization.
RiseUnion	@yangshiqi	Production	Device sharing for third-party hardware (GPU, NPU, MLU, etc.)
Shanghai Aisha Medical Technology Co., Ltd.	@shown1985	Testing	Internal testing.
Sinochem Modern Agriculture	@mazhaoshuo	Production	Inference.
Strongit	@eadou	Testing	Used for testing and training AI algorithms.
Technical University of Munich	@Ajexsen	Evaluation	Master's thesis, federated learning test research and development environment.
Toodata	@51qzpw	Evaluation	Model inference, image interpretation, and other scenarios.
Tongcheng Travel	@devenami	Production	Inference service GPU sharing, improve GPU utilization; GPU model: l40s, a800.
Woqu	@zhuziyuan	Evaluation/Testing	Test GPU sharding
XW Bank	@JJwangbilin	Testing	Solved the problem of GPU computing power isolation.
Xuanyuan Network Technology Co., Ltd.	@15220036003	Evaluation/Testing	Using a physical GPU card for teaching, virtualizing multiple vGPUs for multiple students; the vgpu-device-plugin plugin could not be installed (later resolved with community help).
A certain Chinese enterprise	@18735100708	Production	Deep learning inference.
A certain fund	@hellobiek	Production	Financial scenarios, intelligent customer service, intelligent search, etc.
A certain industrial internet enterprise	@Dravening	Production	Various GPU computing tasks scheduled based on k8s, GPU virtualization greatly helps improve GPU resource utilization.
A certain Shenzhen public institution	@NoKnowKonwNo	Testing	Helm deployment successful; the vgpu-scheduler single pod can only apply for GPU units less than or equal to the number of graphics cards.

HAMi Adopters​

HAMi Adopters