Get game-changing access to compute at scale for high-throughput and low latency AI inference. Purpose-built cloud infrastructure for modern AI workloads, helping you bring innovations to market faster.
Get Started with AI InferenceGet the latest and greatest NVIDIA GPUs, coupled with other cutting-edge hardware components—such as latest generation CPUs and networking interconnects and offered as bare-metal instances.
With no virtualization layer, get full performance out of your compute infrastructure, coupled with industry-leading observability.
Streamline Kubernetes management with pre-installed, pre-configured components via CKS.
With InfiniBand support for multi-node inference—get access to a robust infrastructure for running trillion parameter count AI models in production.
| Feature | PloyD AI Inference | Traditional Cloud | On-Premise |
|---|---|---|---|
| Bare Metal Performance | Full GPU utilization | Virtualization overhead | Direct hardware access |
| Scalability | Instant auto-scaling | Limited by quotas | Manual scaling |
| Cost Efficiency | Pay per use | Reserved instances | High upfront costs |
| Latest Hardware | Always updated | Limited options | Manual upgrades |
| Maintenance | Fully managed | Partially managed | Self-managed |
GenAI models need a lot of data—and they need it fast. Handle massive datasets with reliability and ease, enabling better performance and faster training times. For inference, experience 5x faster model download speeds and 10x faster spin up times.
Our GPU instances provide up to 60TB of ephemeral storage per node—ideal for the high-speed data processing demands of AI inference.
PloyD AI Object Storage is a high-performance S3-compatible storage service designed for AI/ML workloads, with cutting-edge Local Object Transfer Accelerator (LOTA™) technology.
Our Distributed File Storage offering is designed for parallel computation setups essential for Generative AI, offering seamless scalability and performance.
PloyD Tensorizer accelerates AI model loading, so your platform is ready to quickly support any changes in your inference demand.
Tensorizer revolutionizes your workflow by dramatically reducing model loading times. Your inference clusters can quickly scale up or down in response to application demand, optimizing resource utilization while maintaining desired inference latency.
Tensorizer works by serializing AI models and their associated tensors into a single, compact file. This optimizes data handling and makes it faster and more efficient to manage large-scale AI models.
Tensorizer enables seamless streaming of serialized models directly to GPUs from local storage in your GPU instances or from HTTPS and S3 endpoints. This minimizes the need to package models as part of containers, giving you greater flexibility in building agile AI inference applications.
Ditch the case of underutilized GPU clusters. Run training and inference simultaneously with SUNK—our purpose-built integration of Slurm and Kubernetes that allows for seamless resource sharing.
Share compute with ease. Run Slurm-based training jobs and containerized inference jobs—all on clusters managed by Kubernetes.
Effortlessly scale up or down your AI inference workloads based on customer demand. Use remaining capacity to support compute needs for pre-training, fine-tuning, or experimentation—all on the same GPU cluster.
Gain enhanced insight into essential hardware, Kubernetes, and Slurm job metrics with intuitive dashboards.
Work on a platform made to support AI inference, not retrofit for it after the fact.