What is Sesterce Cloud?

GPU Rental - Cloud GPU | Sesterce Compute Clusters, Cloud GPU rental platform offering on-demand GPU instances and bare-metal servers for machine learning, inference, rendering, and high-performance computing. Wide selection of GPU models (A100, H100, L40, RTX4090, A6000, V100, and others) with configurable vRAM and vCPU profiles for training, fine-tuning, and inference workloads.

Launch on-demand VMs and bare-metal servers across multiple regions, with persistent volumes and spot instance options for capacity flexibility. API access and compute cluster controls enable automated provisioning, scaling, and integration with CI/CD and data pipelines.

Instance filters for GPU model, vRAM, vCPU, and region help teams match resources to workload requirements. Suitable for ML engineers, data scientists, AI researchers, and rendering or simulation studios seeking customizable cloud GPU infrastructure.

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Sesterce Cloud's key features

On-demand provisioning of VMs and bare-metal servers
Large catalog of GPU models with selectable GPU counts (1x/2x/4x/8x)
Configurable instance resources: vRAM, vCPU, RAM and persistent volumes
Support for spot instances and bare-metal offers
API (Inference/Storage) with reference documentation for programmatic provisioning

Sesterce Cloud use cases

Train large-scale deep learning models using on-demand A100/H100 GPU clusters with API-driven provisioning, configurable vRAM/vCPU and persistent volumes for fast checkpointing, plus optional spot instances to cut training costs without changing your workflow
Host low-latency GPU inference endpoints on RTX4090/A100 instances or bare-metal servers, automatically scale via the API, store models on persistent volumes for instant rollout, and use spot instances for non-critical batch inference to reduce spend
Run high-performance rendering, VFX or scientific HPC workloads on bare-metal GPU servers or multi-node GPU compute clusters, configure resources per job (vCPU/vRAM/GPU), mount persistent storage for large datasets, and orchestrate distributed jobs through the platform API