Ruchi RoyCPUs can’t keep up with AI workloads—but GPUs can. See how OpenStack, Kubernetes, and PCI passthrough supercharge AI/ML performance while ensuring seamless deployment and scalability.
AI workloads demand serious computational power, and businesses are constantly looking for ways to run them efficiently without overspending. OpenStack, paired with GPU acceleration, provides a powerful infrastructure solution that enhances AI processing while keeping costs under control. Atmosphere offers an OpenStack-powered platform optimized for AI/ML workloads through GPU passthrough, PCI acceleration, and seamless Kubernetes integration.
Why GPUs Matter for AI Workloads
It's no secret that traditional CPUs just can't keep up with the massive parallel processing needed for deep learning, model training, and real-time inference. That’s where GPUs shine. Atmosphere provides dedicated GPU instances across its editions, allowing enterprises to run high-performance AI applications without hitting performance bottlenecks.
How OpenStack and GPUs Work Together
OpenStack makes managing compute, storage, and networking resources dynamic and flexible. When combined with GPUs, it allows organizations to:
- Use PCI Passthrough: Atmosphere supports PCI passthrough, giving AI workloads direct access to GPU resources for maximum performance.
- Deploy AI Workloads on Kubernetes: With OpenStack Magnum, AI applications can be containerized and orchestrated within Kubernetes clusters, making them scalable and efficient.
Keeping Costs in Check
Running AI workloads can be expensive, but Atmosphere’s OpenStack-based approach keeps costs in check by:
- Auto-Scaling & Auto-Healing: Kubernetes automatically adjusts GPU-powered workloads based on demand, preventing over-provisioning.
- Rolling Upgrades: AI workloads can be updated without significant downtime, reducing disruptions.
Storage and Networking Built for AI
AI applications need fast access to vast amounts of data. Atmosphere optimizes storage and networking by offering:
- Block Storage & Ceph Integration: Ceph-powered block storage ensures scalable, high-performance storage for AI models and datasets.
- File & Object Storage: AI applications can store and manage large datasets using NFS-based file sharing or object storage.
- High-Performance Networking: AI models processing real-time data benefit from SR-IOV acceleration, reducing latency and improving efficiency.
Keeping AI Workloads Secure
AI models often process sensitive data, so security is a must. Atmosphere offers:
- Role-Based Access Control (RBAC): Kubernetes environments have fine-grained access controls.
- Identity Management via Keycloak: Supports LDAP, SAML, and OpenID Connect for secure authentication.
- Encryption at Rest & In Transit: Built-in security ensures data protection at all times.
Deploying AI Across Multi-Architectures
AI workloads aren’t always run on the same hardware. Atmosphere supports x86 and ARM architectures, making it easy to deploy workloads across different compute environments.
AI Without Bottlenecks
AI workloads demand high-performance infrastructure that can scale efficiently without breaking budgets. By integrating Kubernetes with GPU acceleration, Atmosphere provides a flexible, scalable, and cost-effective solution for AI/ML applications. Whether you're training deep learning models, running real-time inference, or processing massive datasets, Atmosphere ensures optimal performance, seamless resource management, and easy deployment.
Ready to accelerate your AI workloads? Learn how Atmosphere’s GPU-powered Kubernetes clusters can optimize performance, streamline deployment, and scale effortlessly. Speak to us today to explore the best solution for your AI/ML infrastructure.