Rise CAMP (Computing AI Management Platform) is an enterprise AI orchestration solution built on Rise VAST (HAMi Enterprise Edition), engineered to maximize compute resource efficiency. CAMP provides unified orchestration of heterogeneous AI accelerators through advanced resource pooling, GPU virtualization, and intelligent workload scheduling—reducing infrastructure costs while accelerating AI development cycles. As the core orchestration engine for Rise MAX appliances, CAMP delivers scalable compute foundation for production AI deployments.
GPU utilization improvement
Infrastructure cost reduction
Sub-5 minute deployment
Hardware compatibility
Intelligent scheduling algorithms and GPU virtualization eliminate resource waste, delivering up to 10X improvement in GPU utilization through fractional sharing and workload optimization.
Automated resource provisioning and intelligent workload placement reduce deployment friction, enabling development teams to iterate faster and bring AI solutions to production more rapidly.
Cloud-native architecture with multi-cloud support enables seamless scaling across distributed infrastructure, supporting growth from development clusters to production-scale deployments.
Comprehensive observability, automated operations, and policy-driven governance reduce administrative overhead while ensuring consistent performance and compliance.
Unified orchestration across NVIDIA GPUs, domestic accelerators, and NPUs, creating elastic heterogeneous compute pools with intelligent resource allocation and topology-aware scheduling.
Advanced GPU partitioning with memory oversubscription, fractional allocation, and multi-tenancy support. Optimized for small model workloads with fine-grained resource sharing and isolation guarantees.
Policy-driven scheduling with quota enforcement, priority queues, bin-packing optimization, and workload-aware placement. Supports fixed allocation, fair-share, and guaranteed resource policies.
Purpose-built optimizations for small language models and inference workloads, including GPU multiplexing, batch optimization, and QoS-based resource allocation to maximize throughput per GPU.
Centralized management interface with real-time monitoring, automated scaling, workload migration, and comprehensive observability across distributed AI infrastructure.
Multi-tenant isolation, RBAC integration, comprehensive audit trails, and compliance controls ensuring secure resource sharing and governance across organizational boundaries.
Dedicated technical experts provide deep platform expertise and workload optimization guidance, ensuring your AI infrastructure delivers maximum business value and ROI.
Enterprise SLA with 24/7 monitoring, proactive issue resolution, and guaranteed response times ensure uninterrupted AI operations and business continuity.
Expert guidance on GPU pool architecture, scheduling policy configuration, and workload placement strategies to optimize resource utilization and cost efficiency.
Continuous platform enhancements including new scheduling algorithms, performance optimizations, and security updates delivered through managed upgrade cycles.
Comprehensive training curriculum for platform administrators, ML engineers, and operations teams to master advanced resource management and optimization techniques.
Pre-built integrations with leading MLOps platforms, container orchestrators, and cloud services, enabling seamless workflow integration and multi-vendor interoperability.
Unify GPU resources across on-premises and cloud environments for elastic workload distribution. A global bank deployed CAMP across 600+ nodes, achieving 50% higher resource efficiency.
Optimize inference deployments with intelligent GPU sharing and dynamic scaling. A telecommunications provider operates 500+ model services across 100+ GPU nodes with automated resource allocation.
Accelerate small model development with fractional GPU allocation and rapid iteration cycles. Financial services teams share GPU pools for risk models, achieving 4X higher development velocity.
Seamless orchestration across edge, on-premises, and cloud GPU resources with unified policy management. Manufacturing enterprises achieve 60% cost optimization through intelligent placement.
