Capabilities
Supports a wide range of leading AI accelerators, including NVIDIA, AMD, Huawei Ascend, Iluvatar, MetaX, Cambricon, and Biren.
Automated resource pooling and centralized scheduling abstract away hardware differences, enabling seamless cross-platform use.
Deep integration with upper‑layer workload schedulers enables advanced scheduling strategies and maximizes overall resource efficiency.
High-performance storage and network architecture tailored for AI training and inference,delivering the throughput required for multimodal data processing and large‑scale distributed workloads.
Accelerated distributed training: End‑to‑end optimizations for multi‑node training, including communication optimization, automatic topology tuning, faster data loading, and reduced job startup latency.
Enterprise‑grade fault tolerance: Asynchronous checkpointing saving enables fast save and recovery, complemented by multi‑layer anomaly detection across environments and hardware—supporting minute‑level job recovery and migration.
Comprehensive training observability: Real-time metric tracking, full training‑process visibility, and automated alerts for proactive issue detection.
Native support for advanced algorithms: Non-intrusive infrastructure support for reinforcement learning and embodied intelligence scenarios.
Hyperscale MoE cluster inference：One-click deployment of distributed inference for ultra-large MoE models with 1T+ parameters.
Prefill-Decode decoupled architecture support：Inference cluster configuration management, delivering high stability and optimized performance across text-generation workloads of varying sequence lengths.
Multimodal AIGC optimization：Native support for multimodal workflows such as ComfyUI, enabling automated, asynchronous, batch, high‑concurrency processing, with image storage caching for accelerated inference.
Elastic scaling on demand： Multiple scaling strategies enable expansion to hundreds of instances within minutes, helping absorb sudden traffic spikes.
Platform operations Agent：Automated cluster alert analysis,anomaly detection and remediation, and intelligent node provisioning and deprovisioning.
Operations management Agent：Automated inventory management and intelligent analysis of workload distribution and resource utilization.
Multi-agent collaboration：The Platform Manager Agent works with expert agents such as Resource Management Assistant, Documentation Q&A Agent, and Training Troubleshooting Agent.
Full Lifecycle Support for Enterprise-level Training and Inference
Efficiently build,reliably operate,and intelligently manage large-scale AI infrastructure.
Agents provide continuous,end-to-end assistance—delivering faster deployment, greater stability, and lower operational costs.
Platform Manager Agent
Resource management assistant
Intelligent Q&A on documentation
Training troubleshooting expert
Operation & Maintenance Agent
Cluster alert handling
Intelligent node provisioning and decommissioning
Automatic recovery from failures
Operations Agent
Automatic inventory management
SKU intelligent changes
Campaign price adjustments
Expert Agent Swarm
Multi-expert collaboration
Intelligent decision-making
Efficiency improvement
Advantages
Use Case
A Leading Research Laboratory
The laboratory releases model versions on a fixed schedule and operates clusters with thousands of GPUs to run large language model pre‑training, post‑training, and reinforcement learning workloads.
Training frequently failed to complete within the NCCL 60-minute timeout,triggering repeated fault-recovery cycles and preventing successful job startup.
For MoE pre-training and GRPO on hundreds-of-billions-parameter models,60–70% of 2,000‑GPU jobs stalled after one hour and could not progress.
Data initialization issues that previously caused 60-minute stalls are resolved within 15 minutes.
The same training workloads now run continuously and stably for over two weeks,maintaining consistent throughput.
A Large-model Unicorn Company
Customer scenario: The company manages complex workflows spanning model development and production,with collaboration across multiple teams. Online services combine multiple models,creating intricate inference pipelines with pronounced traffic peaks and troughs.
On‑demand scaling required 1–2 weeks,causing missed traffic opportunities. Maintaining oversized fixed clusters for peak demand resulted in significant idle‑time resource waste and higher costs.
Data processing and model training ran on separate compute environments,fragmenting workflows and requiring artifacts to be transferred across platforms—slowing collaboration and delivery.
Minute‑level elastic scaling enables rapid, flexible resource adjustment, significantly reducing cost.
An integrated training‑and‑inference workflow allows models to be embedded into the company’s proprietary business pipelines in a standardized way, cutting model update and iteration time by 72%.
Infinite Computing, Accelerating the Future of AGI
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