From Resource Allocation to Value Delivery
Unified Management · Full Observability · Fine-grained Slicing · Precise Scheduling · Clear Operations
AI Computing Architecture
Three-layer architecture covering heterogeneous hardware management, intelligent scheduling, and model serving, with integrated O&M and operations for manageable, controllable, operable AI infrastructure.
Rise ModelX
AI as a Service: unified training & inference platform covering the full model lifecycle from development to serving.
Unified Training & Inference
Training and inference managed on a single platform. One-click publishing of trained models as inference services, no duplicate environments or cross-platform migration needed.
Model Hub & Inference
Built-in model marketplace with one-click inference deployment, integrating vLLM, SGLang, MindIE and more.
AI Gateway
Service routing, rate limiting, MCP protocol conversion, and multi-model comparison for enterprise-grade traffic control in the Agent era.
Dual Metering
Resource-based and Token-based billing, with per API-KEY analytics for precise cost accounting.
Heterogeneous Management
Unify different architectures and vendors into a single resource pool
Utilization is the Goal
Maximize hardware ROI and reduce TCO through pooling, slicing, and scheduling
Business Agility is the Outcome
Rapid Agent token serving, cost-aware experimentation, and SLA guarantees for critical workloads
Supported Chips
What Our Customers Say
Real feedback from finance, energy, manufacturing, education and more
"After deploying Rise VAST, our 600+ heterogeneous servers were unified under one management layer for the first time. GPU utilization jumped from under 30% to a steady 70%+ with vGPU slicing and shared pools."
AI Platform Lead · State-owned Bank
"Rise CAMP's priority scheduling solved the resource contention between online inference and offline training across our 100+ server cluster running 500+ model services. No more midnight manual rebalancing."
IDC Operations Manager · Major Telecom
"We used to procure new servers for every AI project. Now with compute pooling, hardware utilization is up 60% and new project onboarding went from 3 weeks to 2 days."
Digital Transformation Director · Manufacturer
"Multiple research groups sharing one GPU cluster used to cause constant conflicts. Rise CAMP's quota management and resource reclamation fixed it completely. Utilization went from 30% to 80%, saving 40% on hardware."
Computing Center Director · Research Institute
"Managing Ascend and NVIDIA GPUs together was our biggest pain point. Rise VAST handles both in one framework. O&M team went from 6 people to 2, fault detection from hours to minutes."
IT Director · Energy Enterprise
"A dozen models for customer service, recommendations, and inventory prediction on one cluster. vGPU slicing cut hardware costs by 30% while actually improving inference response times."
CTO · Retail Enterprise
"Faculty and students sharing compute resources was a constant headache. Rise CAMP's quota management and auto-reclamation policy ended all complaints. Idle GPUs get reclaimed and reassigned automatically."
Lab Director · AI College
"Running risk control, marketing, and customer service models with different SLA requirements on one cluster. Priority scheduling guarantees resources for online risk models while backfilling training jobs during off-peak."
AI Platform Architect · Financial Institution
Proven at Scale
Two Scenarios, Differentiated Management
- ›Short tasks, high frequency, on-demand
- ›Mixed model sizes, high variance
- ›GPU slicing / dynamic scheduling, on-demand
- ›Ready-to-use dev environments, instant compute
- ›Long-running, continuous tasks
- ›High resource and bandwidth usage
- ›Single/multi-node multi-GPU, long occupation
- ›Distributed scheduling, fast fault detection, auto resource reclamation
Use Cases
Unified Training & Inference
Training and inference managed on a single platform. Full pipeline from data processing, fine-tuning to inference deployment, one-click model publishing with no cross-platform migration.
Multi-Model Co-location for Agents
Precisely slice a 7B router, 14B summarizer, and 8B embedding model onto one 80G GPU (20G + 30G + 30G), hard-isolated, turning one card into three.
Domestic Chip Compute Pooling
Unified management of NVIDIA H20, Ascend 910B, KunlunXin P800 multi-architecture clusters, breaking vendor fixed-spec limits with dynamic allocation, utilization reaching 80-90%.
Multi-cluster Unified Operations
Cross-region, cross-architecture multi-K8s cluster management with team and project-based quotas, dual-dimension resource and Token metering for precise cost accounting.
Core Advantages
Self-developed
- • Zero-intrusion vGPU virtualization
- • 10+ domestic chip vendor certifications
- • 10+ software copyrights and patents
Battle-tested
- • Serving PetroChina, State Grid, NSCC and more
- • 6000+ GPU large-scale cluster experience
- • Finance, energy, manufacturing, education
Open Ecosystem
- • HAMi open source core maintainer
- • Volcano scheduler extension support
- • API / WebUI / MCP multi-protocol access
Standards Leader
- • Chair of AI Compute Pooling Workgroup
- • Lead drafter of heterogeneous virtualization standard
- • National Hi-tech Enterprise · ISO 27001 · CMMI3
