Enterprise AI Data Center Solution
d.run helps enterprises build secure, private AI data centers, achieving over 80% resource utilization through intelligent scheduling and sharing. It provides an end-to-end toolchain for LLM training, fine-tuning, deployment, and inference. The Model Store offers real-time updates, accelerating the conversion of computing power into intelligent productivity.
Challenges
Challenges in Heterogeneous GPU management and dynamic scheduling
With multiple GPU architectures in play and ever-changing AI workloads, traditional resource allocation falls behind—lacking the autoscaling needed for today’s AI demands.
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Low inference efficiency
The issues like insufficient model throughput and high latency create a significant gap between inference efficiency and corporate expectations.
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Gap from computing to application
The lack of synergy across hardware, frameworks, and applications results in inefficient compute usage and unpredictable business returns.
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Data security and privacy protection
Industries like finance and government have high data sensitivity, requiring private deployment to keep core data local.
Solution and advantages
The world's leading compute scheduling engine, efficiently optimizing resource utilization
- No.1 GPU-scheduling Kubernetes provider in Asia, with decade-long expertise in large-scale cluster services.
- Pioneer in open-source scheduling technologies: KWOK, Spiderpool, HAMi, and Kueue.
- Unified manages diverse GPUs such as NVIDIA, MetaX, Enflame, eliminating hardware heterogeneity.
From popular frameworks to leading inference solutions
- Support inference frameworks like vLLM, SGLang, boosting inference efficiency by 30%.
- Pioneering AI inference in open source：co-developer of LWS and major contributor to vLLM and SGLang.
Easy-to-use services powered by an open LLM ecosystem
- Provide diverse services compatible with both self-developed and open-source LLMs, helping build an independent AI ecosystem without vendor lock-in.
- Support API integration for leading models, and offer plugins and orchestration tools to accelerate industry deployment.
One-stop LLM development, easy for developers to get started.
- An end-to-end platform for data prep, LLM training, fine-tuning, and deployment.
- Support frameworks such as PyTorch, TensorFlow, and PaddlePaddle, along with scheduling policies like distributed training, checkpoint recovery, queue management, and resource preemption.
- Autoscale to handle traffic spikes, ensuring flexibility and high concurrency.
Private deployment ensures the security of enterprise data
- On-premises deployment within corporate intranet guarantees data confinement, ensuring secure local operations to meet stringent privacy requirements.
- Exclusive enterprise control over data and workflows ensures full autonomy without external interference.
Customer Story
d.run helps Shanghai Cube to create an integrated platform for pooling, utilizing, and managing computing power. It enhances cluster utilization, supports flexible external services, and optimizes LLM services. This drives improved user experiences and helps enterprises achieve intelligent upgrades.
