Rise CAMP
AI Computing Power Scheduling Platform: Unified management and scheduling of heterogeneous computing resources, simplifying AI application development and deployment
Rise VAST
AI Computing Power Management Platform: Pooling and virtualization of heterogeneous GPU resources to improve resource utilization(HAMi Enterprise Edition)
Rise Model X
AI Model Management Platform: Integrated solution designed for enterprise AI computing scenarios, reducing the cost of AI application development and deployment
Rise MAX
AI Computing Power Management Appliance: Integrated solution designed for enterprise AI computing scenarios, reducing the cost of AI application development and deployment
Rise ModelX
Compiling AI optimization and technical guides
to accelerate customer AI infrastructure development.
RiseUnion leverages innovative AI to deliver efficient, flexible, and secure computing resource management,
driving digital transformation and intelligent industry growth.
GPU Virtualization Deep Dive: User-space vs Kernel-space Solutions
Navigating the Compute Challenges in the AI Cloud-Native Era
NVIDIA Acquires Run:ai: Deep Integration of AI Infrastructure
Fine-Tuning Mainstream LLMs on the Ascend Platform with Torchtune
[Q&A] HAMi Frequently Asked Questions - Series 1
Break the Misconception! GPU Pooling for Accelerated AI Training
Full-model Fine-tuning vs. LoRA vs. RAG
Understanding the Role of RAG, Fine-Tuning, and LoRA in GenAI
Project HAMi: Heterogeneous AI Computing Virtualization Middleware
How GPU Virtualization Technology Can Save You Money!
HAMi Community First Offline Salon Successfully Held
HAMi vGPU code Analysis Part2: hami-webhook
HAMi vGPU code Analysis Part 1: hami-device-plugin-nvidia
Open Source vGPU Solution HAMi: Core & Memory Isolation Test
Why K8s Cannot Meet AI Computing and Large Model Scheduling Needs
Complete Guide to PyTorch Distributed Training: From Basics to Mastery
Multi-GPU Deep Learning Guide: Model & Data Parallelism Explained
Why Top AI Companies Choose Kubernetes
HAMi 2.4.0 Major Update: Making AI Computing Management More Efficient