Is vGPU a False Promise? Why Doesn't NVIDIA Offer Fine-Grained Sharing Itself?
Hello everyone, I'm a developer from the HAMi community.
In many discussions about HAMi's virtualization technology, I often see two noteworthy types of comments: one is a natural technical inquiry, such as: "Since CUDA is NVIDIA's own technology, why doesn't the company officially provide fine-grained GPU sharing capabilities like HAMi?", and the other is a more firm assertion, like: "vGPU is a pseudo-problem; in real-world scenarios, everyone is eager to run a full GPU at maximum load for days on end."
Actually, I used to wonder about these questions myself. After being involved with the HAMi project for a while, I've gradually developed some observations and thoughts. I wanted to take this opportunity to organize and share my understanding, and I welcome everyone to exchange ideas, add to the discussion, and debate.
Regarding this topic, I've structured my thoughts around a few key areas:
1. What is HAMi, and how does it fundamentally differ from NVIDIA's solutions?
2. Why doesn't NVIDIA "do it"? Is it a technical limitation or a business decision?
3. What are HAMi's core competitiveness and long-term moat?
Some of these are open-ended questions, and I certainly don't have all the answers myself, but I hope this short article can offer a perspective. If you're also interested in AI Infra, GPU virtualization, or unified management of heterogeneous computing, I invite you to join the conversation.
What is HAMi? How Does It Fundamentally Differ from NVIDIA's Solutions?
Before we discuss the Why (Why NVIDIA 'doesn't do it'), let's first clarify the What:
What do NVIDIA's existing GPU sharing/virtualization solutions cover, and where are the gaps?
What is HAMi's differentiated positioning in the GPU virtualization space?
Many articles have analyzed the technical details of the four main solutions—Time-Slicing, MPS, MIG, and vGPU—so I won't repeat them here. Instead, I'll just summarize their boundaries and limitations:
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Time-Slicing: Software-level, round-robin sharing. Good compatibility but no isolation, making performance unpredictable. Suitable for development and testing.
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MPS (Multi-Process Service): Multiple processes share a GPU context for concurrent execution, improving throughput. However, it lacks memory isolation, making it unsuitable for multi-tenant environments.
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MIG (Multi-Instance GPU): Hardware-level, fixed partitioning. Provides strong isolation and predictable performance, but it's limited to high-end GPUs and the partitioning is not very flexible.
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vGPU: A commercial solution based on a hypervisor. It allocates resources through predefined profiles, offering strong isolation but with fixed granularity and requiring a license. It's mainly used in platforms like VMware, Citrix, and OpenStack. In a Kubernetes context, it requires introducing VMs via KubeVirt, which complicates deployment and reduces native support.
The solutions above each make different trade-offs between isolation, granularity, flexibility, and cost. This naturally highlights a gap in the market: the need for flexible, fine-grained partitioning (down to 'Compute % + Memory MB' levels) and isolation for a wide range of NVIDIA GPU models within Kubernetes, all while being non-intrusive and requiring zero application code changes. This is precisely where HAMi fits in: open-source, fine-grained, low-barrier, and balancing both isolation and flexibility.
So, the question arises: if this gap is so obvious, why hasn't NVIDIA filled it?
Why Won't NVIDIA Do It? Is It a Technical Limitation or a Business Decision?
NVIDIA controls the entire technology stack. If it were a strategic priority, they could overcome any technical hurdles, whether it's legacy baggage or maintenance costs. Clearly, technology isn't the primary obstacle. The real considerations are business and ecosystem.
The primary business consideration is protecting its carefully constructed, high-profit moat. MIG technology is a core selling point for its Ampere and later data center GPUs, justifying the premium price of high-end cards. If an official software solution allowed mid-to-low-range cards to achieve similar fine-grained partitioning, it would undoubtedly improve sharing efficiency, but it would also dilute the uniqueness of MIG and potentially erode the profit margins of the high-end market. At the same time, the vGPU business has been tied to a commercial licensing model with partners like VMware and Citrix for years. A free, container-native official solution with overlapping features would be akin to a "clearance sale" of their own products. It would directly impact existing vGPU revenue and disrupt these partnerships.
Maintaining a clear product line positioning and market segmentation is also crucial. A highly flexible and universal software sharing capability could blur the lines between consumer-grade GeForce cards and professional/data center cards, making the former "good enough" for some server scenarios and thereby affecting the sales and pricing strategies of the latter.
This is similar to how Apple intentionally reserves certain features for its high-end models—maintaining product tiers and protecting high-profit margins are the key business considerations.
Furthermore, maintaining its vast ecosystem and partner relationships is another strategic priority. Damaging the deep alliances built with partners around vGPU would be a greater loss for NVIDIA. NVIDIA's recent acquisition of Run:ai and its subsequent decision to open-source its scheduling component, KAI-Scheduler, while keeping the runtime-level memory isolation code closed-source, is a telling strategic move. It seems that in the K8s ecosystem, NVIDIA prefers to provide capabilities that are "useful, but not overly so," improving resource utilization through high-level scheduling optimizations. This avoids disrupting its existing commercial products (vGPU, NVIDIA AI Enterprise, etc.) and partner ecosystem, leaving enough market space for them.
To sum it up in one sentence
For NVIDIA, "not doing it" isn't a matter of technical inability but a comprehensive business choice to protect its high-end hardware premiums, vGPU licensing revenue, and critical ecosystem partnerships—and this is precisely the opportunity that gives HAMi room to thrive in the container-native, fine-grained sharing space.
What Are HAMi's Core Competitiveness and Long-Term Moat Truly?
Now that we've discussed why NVIDIA "doesn't do it," the next natural question is: if this is primarily a business decision, what is the foundation of HAMi's existence? After all, techniques like CUDA API interception are not some unattainable "magic." What if NVIDIA has a change of heart and enters the market itself one day, or if other powerful competitors emerge? Can HAMi's advantages be sustained? And where does our "moat" lie?
In my view, HAMi's competitiveness doesn't come from a single technical breakthrough but from a combination of several factors. First, we have successfully integrated the core technology of CUDA API interception with the K8s ecosystem, providing a practical and flexibly configurable solution for GPU virtualization and enhanced scheduling. This in itself is a complex engineering practice geared toward production readiness.
More importantly, HAMi has a very unique advantage: broad compatibility with heterogeneous hardware. In addition to NVIDIA GPUs, HAMi was designed from the ground up to support, and already supports, various domestic AI chips (such as Cambricon, Hygon, Tianshu Zhixin, Moore Threads, Ascend, Biren, etc.). In the context of China's emphasis on supply chain security and domestic technology adaptation, the need to uniformly manage heterogeneous computing resources and reduce dependency on a single supplier is a real and increasingly pressing pain point, and a market that a single hardware vendor like NVIDIA cannot fully cover.
So, in the long run, what constitutes HAMi's "moat"? Frankly, with various AI tools lowering the barrier to learning and building new technologies, simply emphasizing "how high the technical barrier is" may not be a reliable strategy.
I believe the real barrier lies more in those elements that are difficult to replicate quickly. As a CNCF Sandbox project, HAMi has already built some recognition and influence in the cloud-native community. Our integrations with projects like Volcano and Koordinator, our ongoing collaboration with KAI-Scheduler, and our growing community of users and contributors are gradually forming an ecosystem. Earning the trust of this circle takes time.
At the same time, support for various heterogeneous hardware, especially domestic chips, has given HAMi strategic depth and a unique value proposition in specific markets (especially in China). As long as this demand exists, HAMi will have its own irreplaceability.
But among all these, I believe the most important moat is the customer trust and product stability accumulated through large-scale, real-world deployments. When enterprises choose infrastructure software, what ultimately matters is stability, reliability, and good support. The practices and evaluations by leading companies like Huawei, SF Technology, and iFLYTEK... This process of gradually building trust, refining maturity, and accumulating operational experience is the hardest thing to replicate quickly. Even with similar technology, getting enterprise customers to confidently use it in a production environment still requires crossing a very high trust threshold.
So, regarding the concern of "will NVIDIA enter the market itself?", my personal judgment, based on their strategy after acquiring Run:ai, is that it's unlikely in the short term. Even if things change in the future, HAMi, with its heterogeneous support and existing market foundation, is not without a fighting chance. The real challenge lies in whether we ourselves can continuously refine our product, earn the deep trust of more customers, and create more successful benchmark cases.
Conclusion
Returning to the original question, I believe that vGPU, or fine-grained GPU sharing, is by no means a false promise, but rather a technical direction born from real market demand. NVIDIA's choice not to pursue it is more a reflection of its own strategic and business trade-offs.
And HAMi's value lies precisely in its ability to target the demand space "left blank" by NVIDIA, especially now that heterogeneous computing management and domestic adaptation have become important topics. Its strength comes from the organic combination of technical implementation, heterogeneous compatibility, and ecosystem integration; and its long-term foundation, more than the technology itself, will be built on the customer trust, product stability, and deep market integration gained through large-scale, real-world deployments.
Of course, the above are just some of my own preliminary thoughts as a community developer, based on currently available information and practical experience. I am very much looking forward to hearing more insightful perspectives from friends in the community, and I welcome everyone to exchange ideas, offer critiques, and discuss together to collectively push this field forward.