In the rapidly expanding universe of large AI models, the insatiable demand for GPU computing power has created a profound paradox where scarcity coexists with immense waste. While these powerful processors have become strategic assets, their actual utilization rates often languish in the low double digits, a staggering inefficiency that threatens to stifle innovation. Addressing this critical challenge head-on is Dynamia.ai, a pioneering company focused on heterogeneous computing power virtualization, which recently announced the completion of a significant angel-round financing of tens of millions of yuan. The investment, led by Fosun Capital with participation from Zhuopu Capital and existing seed investors, signals strong market confidence in the company’s vision. This new capital injection is earmarked to expand the ecosystem surrounding its cornerstone open-source project, HAMi, and to accelerate the commercialization of its enterprise-grade scheduling platform, aiming to unlock the full potential of the world’s computing resources.
Confronting the Fragmentation Dilemma in AI Infrastructure
The fundamental problem Dynamia.ai is solving stems from the growing complexity and fragmentation of modern AI infrastructure, a challenge that has become a primary bottleneck in the development of next-generation systems. As enterprises increasingly integrate a diverse array of AI chips from both domestic and international manufacturers—including GPUs and accelerators from NVIDIA, Huawei Ascend, Moore Threads, and Cambricon—they encounter a “fragmentation dilemma.” This heterogeneous environment, where hardware from different vendors with distinct architectures must operate in tandem, creates severe operational hurdles. The inability to perform unified scheduling across these disparate resources, coupled with inefficient sharing mechanisms, results in the persistently low utilization rates that plague the industry. These obstacles not only inflate operational costs but also limit the scale and speed at which new AI applications can be developed and deployed.
To dismantle these barriers, Dynamia.ai initiated and now leads HAMi, the only project within the prestigious Cloud Native Computing Foundation (CNCF) dedicated to heterogeneous computing power virtualization. HAMi’s ambitious goal is to establish a universal standard—a “unified language” for computing power—that can effectively transform a disjointed collection of siloed hardware into a fluid and unified resource pool. By championing an open-source approach, the company aims to foster a collaborative and interoperable future, breaking down the proprietary walls that currently segment the market. This strategy is seen not just as a technical solution but as a crucial enabler for the global adoption of diverse hardware, creating a level playing field where innovation can thrive regardless of the underlying chip architecture.
A Technical Revolution in Resource Management
The architectural design of HAMi represents a fundamental paradigm shift, moving the industry away from rigid, “static exclusive” resource allocation toward a more agile “dynamic decoupling” model. It achieves this transformation through a sophisticated, deep-level virtualization and pooling management system that abstracts computing resources from the physical hardware. At its core are several robust technical capabilities, including fine-grained segmentation. This feature allows for the partitioning of a single GPU’s resources, including both its video memory and computing cores, with remarkable precision—down to one-tenth of the card’s capacity or even smaller increments. This is complemented by an advanced “video memory over-provisioning” mechanism, which intelligently allows multiple high-concurrency tasks to share a single GPU without interference, dramatically increasing the number of tasks a single piece of hardware can handle and directly boosting its overall utilization.
Further enhancing its capabilities, the platform provides extensive hardware support, having been successfully adapted to over nine different types of chips from a wide array of manufacturers. This enables computing power from vastly different architectures to be ingested into a single, standardized resource pool for unified management and scheduling. The system also supports dynamic Multi-Instance GPU (MIG) flexible configuration, allowing administrators to re-partition GPUs on the fly to meet the specific demands of varying workloads. For the modern cloud-native world, HAMi offers seamless, “zero-intrusion” integration with the Kubernetes ecosystem, meaning developers can leverage its power without modifying existing application code. This is paired with intelligent, automated resource management, including the automatic elastic scaling of video memory and a task priority preemption mechanism that protects mission-critical applications when resources become scarce.
From Open-Source Vision to Commercial Viability
The practical benefits of HAMi’s technology have been clearly validated through real-world enterprise deployments that demonstrate a significant return on investment. In a compelling case study with SF Technology, the platform’s implementation enabled the deployment of 19 distinct test services on just six GPUs. This not only represented a hardware reduction of 13 GPUs but also resulted in a resource efficiency increase of more than double, showcasing a direct impact on capital expenditure and operational costs. Similarly, for the Vietnamese AI learning platform PREP EDU, which operates a complex mixed-GPU environment of RTX 4070 and 4090 cards, adopting HAMi’s vGPU scheduling capabilities delivered profound results. Combined with the DevOps team’s workflow optimizations, the solution led to a 50% reduction in GPU cluster management pain points and a remarkable 90% optimization of the underlying GPU infrastructure.
Beyond its success as an open-source project, Dynamia.ai has already proven its commercial viability by building a suite of enterprise-level products and technical services around the HAMi core. These commercial offerings provide enhanced engineering capabilities, stability support, and ongoing maintenance tailored for demanding production deployments. The market’s appetite for such a solution became evident quickly; within its first quarter of commercial operations, Dynamia.ai secured product contracts valued at 2 million yuan. In a further validation of its industry relevance, the company also gained active adaptation support from AWS for its inference chips, confirming that its technology is not only in demand by end-users but is also recognized by major cloud providers as a critical component in the evolving AI ecosystem.
An Endorsed Vision for the Future of Computing
The driving force behind Dynamia.ai is a founding team with deep and proven expertise in cloud computing, cloud-native technologies, and AI infrastructure. CEO Zhang Xiao previously led the container team at the cloud-native leader DaoCloud, while co-founder and CTO Li Mengxuan was the head of heterogeneous computing power technology at Fourth Paradigm. Both are seasoned contributors to the open-source community, underscoring their commitment to a collaborative development model. Founder Zhang Xiao articulated a patient, long-term vision, stating that “heterogeneous computing power pooling technology is not only a tool for improving efficiency but also the ‘last mile’ for domestic chips to enter the mainstream production environment.” The company’s strategy prioritizes establishing HAMi as the industry’s de facto standard over aggressive short-term monetization, with the ultimate goal of making computing power as simple and reliable as a utility like water or electricity.
This strategic vision and technical execution received strong endorsements from the investment community, which recognized the company’s potential to reshape the industry. Ye Lijuan of Fosun Capital emphasized that heterogeneity is the long-term future of the computing market and that Dynamia.ai provided an indispensable link between hardware and applications. Chen Minjie of Zhuopu Capital drew a powerful parallel, suggesting that just as VMware had become the virtualization giant for CPUs in the cloud era, a similar transformative leader was needed for GPUs in the AI era. He further noted that for the domestic chip industry, an open standard like HAMi was a “necessity for survival,” enabling diverse hardware to break free from proprietary ecosystems and resonate globally. Through this lens, HAMi was positioned to become the universal standard for heterogeneous computing power scheduling worldwide.
