What Virtualization Options Are Available for H200 GPU?

Cyfuture Cloud supports NVIDIA H200 GPU virtualization primarily through Multi-Instance GPU (MIG) technology, enabling up to 7 isolated instances per card for secure multi-tenancy. Additional options include GPU Droplets as virtual machines and scalable multi-GPU clusters with NVLink interconnects.​

Key Virtualization Options for H200 GPU on Cyfuture Cloud:

These leverage H200's 141GB HBM3e memory for memory-intensive tasks like LLMs (70B+ parameters).​

Overview of H200 GPU

The NVIDIA H200 Tensor Core GPU, released in mid-2024, features 141GB HBM3e memory and 4.8 TB/s bandwidth, doubling inference speed over H100 for generative AI and HPC. Cyfuture Cloud delivers it via GPUaaS, eliminating hardware ownership with hourly/monthly pricing. MIG virtualization slices the GPU into isolated partitions, each with dedicated compute/memory, preventing noisy neighbor issues in cloud setups.​

Cyfuture integrates H200 in HGX platforms with NVLink for low-latency multi-GPU communication. Users deploy via intuitive dashboard, selecting MIG profiles (e.g., 1/7th to full GPU) for workloads like LLM fine-tuning or simulations. Global data centers ensure low latency, with NVMe/object storage and Kubernetes compatibility.​

Deployment and Scaling

Launch H200 GPU Droplets in minutes: choose single-node for prototyping or clusters for distributed training. MIG enables fractional GPU use, optimizing costs—e.g., run 7 inference pods simultaneously. Scaling is dynamic, from 1-8 GPUs per VM to InfiniBand-like clusters supporting NCCL libraries for PyTorch/TensorFlow.​

Security includes biometric access, end-to-end encryption, and MIG isolation for confidential computing in finance/healthcare. Power efficiency cuts usage by 50% vs. prior gens, with 24/7 support for seamless ops. Customize with 16-64 vCPUs, 128-512GB DDR5 RAM, and high-bandwidth networking.​

Benefits for AI/HPC Workloads

H200 excels in memory-bound tasks: train larger models without multi-GPU offloads, accelerate RAG pipelines, or simulate complex physics. On Cyfuture, MIG virtualization maximizes utilization—up to 7x density—reducing costs 60% vs. on-prem. Frameworks like RAPIDS/CuPy run natively, with NVSwitch for 1.8 TB/s scaling.​

Conclusion

Cyfuture Cloud's H200 virtualization centers on MIG for efficient, secure sharing, complemented by flexible Droplets and clusters for any scale. This GPUaaS model empowers AI innovation without CapEx, delivering 2x H100 performance for production LLMs and HPC. Start with pay-as-you-go to future-proof workloads.​

Follow-Up Questions

Q: How do I enable MIG on H200 GPUs?
A: Via Cyfuture dashboard, select MIG mode during Droplet creation; NVIDIA tools like nvidia-smi configure partitions (e.g., 3g.20gb profiles). Supports up to 7 instances with memory pinning.​

Q: What workloads suit MIG virtualization?
A: Inference servers, batch jobs, dev environments; isolates noisy ML training from stable production APIs.​

Q: Can I integrate H200 with Kubernetes?
A: Yes, Cyfuture provides managed K8s with GPU scheduling; use NVIDIA operators for MIG-aware pods.​

Q: What's the pricing for H200 virtualization?
A: Hourly pay-as-you-go (from ~$X/GPU-hour, varies by config); no long-term commitments, scales with usage.​

Q: How does H200 MIG compare to vGPU?
A: MIG offers hardware-level partitioning (better isolation/perf) vs. software vGPU; H200 prioritizes MIG for AI clouds.​