NVIDIA RAPIDS
The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
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NVIDIA Base Command
NVIDIA Base Command™ is a software service for enterprise-class AI training that enables businesses and their data scientists to accelerate AI development. Part of the NVIDIA DGX™ platform, Base Command Platform provides centralized, hybrid control of AI training projects. It works with NVIDIA DGX Cloud and NVIDIA DGX SuperPOD. Base Command Platform, in combination with NVIDIA-accelerated AI infrastructure, provides a cloud-hosted solution for AI development, so users can avoid the overhead and pitfalls of deploying and running a do-it-yourself platform. Base Command Platform efficiently configures and manages AI workloads, delivers integrated dataset management, and executes them on right-sized resources ranging from a single GPU to large-scale, multi-node clusters in the cloud or on-premises. Because NVIDIA’s own engineers and researchers rely on it every day, the platform receives continuous software enhancements.
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OneSource Cloud
OneSource Cloud designs, builds, and manages sovereign AI infrastructure for organizations in regulated industries that cannot run sensitive workloads on public cloud: healthcare and life sciences, financial services, government and defense, energy, legal, and research.
We deliver dedicated GPU compute as a managed service. Scope covers cluster design, hardware procurement, data center colocation, deployment, and ongoing operations. Clusters use NVIDIA GPUs with InfiniBand interconnect for multi-node training and inference, backed by high-performance storage and private networking. Each customer gets an isolated, single-tenant environment, so data and models never share hardware with another tenant.
Managed services include capacity planning, provisioning, workload scheduling, monitoring, patching, and support. Environments are configured to the customer's compliance requirements, including NIST 800-171 and data residency controls.
We operate 20,000+ GPUs across 96+ DCs
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Sangfor aStor
Sangfor aStor is a software‑defined storage solution that unifies block, file, and object storage into a single, elastically expandable resource pool using a fully symmetrical distributed architecture, enabling on‑demand allocation of high‑performance and cost‑optimized, large‑capacity tiers to suit diverse service requirements. Available as either integrated hardware‑software or standalone software, it scales from just three commodity x86 nodes and supports cloud‑scale clusters of thousands of nodes with EB‑level capacity expansion. Its multi‑node parallel processing and intelligent caching (using RDMA, SSD hot‑data cache, and layering) deliver extremely high throughput, IOPS, and small‑IO performance, boosting cache hit rates to 90% and small‑IO handling by up to 65%, while distributed metadata management ensures jitter‑free handling of billions of files.
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