Model Inference Solution
GPU Cloud Infrastructure for High-Concurrency, Low-Latency AI Inference
Scene Description
Designed for enterprise AI deployment scenarios such as API-based services and real-time AI applications (e.g., intelligent assistants, content generation, vision and speech inference),
inference systems typically face challenges including high concurrency, latency sensitivity, fluctuating compute demand, and increasing cost pressure.
This solution is purpose-built for inference workloads,leveraging high-performance GPU cloud infrastructure,inference optimization frameworks, and global node deployment to deliver stable, scalable, and low-latency AI inference services.
Technical Capabilities
- Full support for leading inference optimization frameworks such as Triton Inference Server, TensorRT, and vLLM, significantly improving throughput and response time
- Global multi-region inference node deployment enables proximity-based access, achieving millisecond-level end-to-end latency
- Built-in high availability with automatic failover and elastic scaling, delivering up to 99.99% service uptime
- Dynamic compute scheduling that automatically scales GPU resources based on real-time traffic demand, preventing overprovisioning and performance bottlenecks
Recommended Configuration
Cost Efficiency
From hardware investment and billing accuracy to long-term procurement, a cost advantage is built across the entire chain, making computing power usage more economical and efficient.
On-demand GPU inference resources eliminate long-term capacity lock-in
Inference-optimized architecture reduces costs by 30%–50% compared to training environments
Flexible hourly or usage-based billing models align with different business growth stages
No minimum commitment, enabling enterprises to maintain full cost control
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