How can I run multi node GPU clusters in GPU as a Service?

Cyfuture Cloud enables running multi-node GPU clusters through its scalable GPU as a Service (GPUaaS) platform, supporting NVIDIA H100, A100, and other GPUs with high-speed InfiniBand networking and orchestration tools like Kubernetes and Slurm.​

Sign up on Cyfuture Cloud dashboard, select GPU cluster configuration (e.g., 4-8 GPUs per node with 200Gbps InfiniBand), deploy via one-click provisioning using Kubernetes or Slurm, install AI frameworks like PyTorch with NCCL for multi-GPU communication, and monitor with Prometheus/Grafana. Scale horizontally up to 1000+ nodes on-demand.​

Overview of Cyfuture Cloud GPUaaS

Cyfuture Cloud's GPU as a Service provides access to enterprise-grade NVIDIA GPUs including H100, H200, L40S, A100, V100, and T4, optimized for AI training, inference, and HPC workloads. Multi-node clusters connect these GPUs via 200Gbps InfiniBand or 400Gbps Ethernet with RDMA support, ensuring low-latency (<1µs) inter-node communication essential for distributed training. Each node features AMD EPYC or Intel Xeon CPUs, up to 2TB DDR5 RAM, and NVMe SSD storage with Lustre/GPFS parallel file systems for high-throughput data handling.​

The platform supports flexible scaling from single GPUs to 1000+ node clusters, with pay-as-you-go pricing starting at $0.57/hr for L40S and $2.34/hr for H100 instances. Pre-configured environments include CUDA 12.x, cuDNN, TensorFlow, and PyTorch, enabling 5x faster ML model deployment compared to traditional setups.​

Step-by-Step Setup Guide

Begin by creating an account on the Cyfuture Cloud portal and navigating to the GPU section to select a cluster plan based on GPU type, node count, and storage needs. Choose configurations like 4-8 GPUs per node with NVLink interconnects for intra-node performance up to 900GB/s bandwidth.​

Deploy the cluster with one-click provisioning, which automates hardware allocation and software stack installation including NVIDIA GPU Operator for Kubernetes or Slurm for HPC scheduling. Connect via SSH, upload datasets and Docker containers, then initialize multi-node communication using NCCL for collective operations in PyTorch DistributedDataParallel or Horovod.​

For example, launch a PyTorch job across nodes: torchrun --nnodes=4 --nproc_per_node=8 train.py. High-speed networking ensures efficient gradient synchronization in large-scale training.​

Key Features and Technical Specs

Cyfuture Cloud clusters deliver FP64 performance of ~20 TFLOPS per A100 GPU and up to 624 TOPS for H100 inference in FP16/INT8.​

Security includes AES-256 encryption, ISO 27001/SOC 2 compliance, and RBAC for multi-tenant isolation. Deployment options span cloud, on-prem, or hybrid for data sovereignty.​

Optimization and Best Practices

Leverage MIG on A100/H100 GPUs to partition single GPUs into up to 7 instances for efficient multi-workload hosting. Use NVIDIA GPU Operator for seamless Kubernetes integration, enabling dynamic scaling and fault tolerance.​

Monitor with custom Grafana dashboards to optimize resource utilization, targeting >80% GPU occupancy. For LLM fine-tuning, employ DeepSpeed or Megatron-LM libraries integrated with the pre-installed stack. Test scaling incrementally: start with 2 nodes, validate NCCL benchmarks, then expand.​

Conclusion

Cyfuture Cloud simplifies multi-node GPU clusters in GPUaaS with turnkey infrastructure, reducing setup time from weeks to minutes while cutting costs by up to 60-70% versus on-prem hardware. This enables enterprises, researchers, and startups to focus on AI innovation rather than infrastructure management, supporting workloads from model training to real-time inference at scale.​

Follow-Up Questions

What GPU models are available?
Cyfuture offers NVIDIA H200 (141GB HBM3e), H100 (80GB), A100 (40/80GB), L40S (48GB), V100 (32GB), T4 (16GB), plus AMD MI300X and Intel Gaudi 2.​

How much does it cost?
Pricing is pay-as-you-go: H100 from $2.34/hr, L40S from $0.57/hr; reserved instances offer discounts for long-term use.​

Is Kubernetes supported?
Yes, full integration with NVIDIA GPU Operator for containerized multi-node deployments.​

What about security and compliance?
Features AES-256 encryption, RBAC, and ISO 27001/SOC 2/HIPAA compliance.​