Rent NVIDIA H100 SXM Servers

A single 1× H100 SXM instance (1H100.16v.256m) starts at $3.66/hr on-demand, billed per second from launch to termination. Reserved pricing cuts that to $2.92/hr on a 6-month commitment or $2.43/hr annually. The 2× H100 node — the most-rented configuration — runs $7.23/hr on-demand, dropping to $4.67/hr on a 12-month reservation. The 8× H100 node starts at $28.36/hr on-demand and goes as low as $18.29/hr on a 12-month reservation — that's over $88,000 per year in savings compared to on-demand at the same run time.

It depends entirely on the workload — and we'd rather give you an honest answer. For models above 30B parameters, H100's FP8 Transformer Engine delivers 2–2.5× higher effective training throughput, which often means a shorter run time and lower total cost even at the higher hourly rate. For inference on 70B+ models, vLLM on H100 consistently delivers 2–3× higher token throughput, cutting per-token serving cost at scale. For smaller models under 13B, LoRA fine-tuning, or cost-sensitive burst jobs, A100 remains the better value. If you're unsure, reach out — our team will do a workload analysis before you commit.

Under 60 seconds from console click to SSH on existing verified accounts. New accounts go through a one-time KYC check that takes under 10 minutes during business hours. After that, you can launch any 1× to 8× H100 SXM configuration on-demand with no quota request or capacity pre-approval. Pre-built images for PyTorch 2.5+, vLLM 0.6, TensorRT-LLM, and NeMo mean you skip stack provisioning entirely and start your job immediately.

The Transformer Engine is a hardware unit on Hopper that dynamically switches between FP8 and BF16 precision on a per-layer, per-step basis during training — automatically, with no manual tuning. The result is near-FP8 throughput (989 TFLOPS versus 312 TFLOPS FP16 on A100) with BF16-grade accuracy on standard language model workloads. It's not a marketing claim — teams running Llama 3 pre-training and Mixtral fine-tuning on Cyfuture H100 instances measure 1.8–2.4× faster wall-clock training versus the same model on A100, same configuration, same framework.

Yes. A single node supports up to 8× H100 SXM5 with the full NVLink 4.0 mesh at 900 GB/s — enough for 70B model training with no inter-node communication at all. For larger runs across 16, 32, or 64 GPUs, we connect nodes over 200/400 Gbps InfiniBand with NCCL-tuned topology. Contact our enterprise team for cluster reservations — setup time for configurations under 64 GPUs is typically 4–6 hours including network fabric validation and NCCL ring testing.

Multi-Instance GPU on H100 lets you split a single card into up to 7 hardware-isolated compute instances, each with its own dedicated HBM3 memory slice, L2 cache, and SM compute allocation. Isolation is at the hardware level — physically separate circuits, not virtualization. Workloads on different MIG slices cannot access each other's memory or compute resources under any conditions. Slices start at $0.55/hr for a 1g.10gb partition — ideal for multi-tenant inference, CI/CD pipelines, or development environments where a full 80GB card is overkill.

CUDA 12.4, cuDNN 9, NCCL 2.20, and Triton 3.x on all H100 images. Pre-built stacks include PyTorch 2.5+, TensorFlow 2.18, JAX 0.4, vLLM 0.6, TensorRT-LLM 0.13, and NeMo 2.0. BYO container images are fully supported via OCI registry — both Docker Hub and NVIDIA NGC. If you have a custom container with specific library pinning, you can bring it directly — no re-packaging required.

Cyfuture AI operates Tier III+ data centres in Noida, Bangalore, and Delhi NCR — all with sub-5ms latency to major Indian metros. We're ISO 27001, SOC 2 Type II, and DPDP-compliant. INR billing is available with full GST invoicing. For international workloads, our Noida facility maintains sub-200ms round-trip latency to Singapore, Dubai, and Frankfurt. All data processed on Indian instances stays within India unless you configure cross-region transfer explicitly.