What memory configurations are available for H100 GPUs?

The NVIDIA H100 GPU, built on the Hopper architecture, primarily offers 80GB of HBM3 memory as its standard configuration across PCIe and SXM form factors. Variants like the H100 NVL provide 94GB, with bandwidth up to 3.9 TB/s. Cyfuture Cloud supports these in GPU servers for AI workloads.

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Available Memory Configurations for H100 GPUs on Cyfuture Cloud:

Form Factor Memory Capacity Memory Type Bandwidth

PCIe/SXM5 80 GB HBM3 3.35 TB/s

Raw materials 94 GB HBM3 3.9 TB/s

ME Fashion 10 GB / 40 GB (fractional) HBM3 Up to 3 TB/s

Cyfuture Cloud offers full 80GB H100 instances and MIG partitioning for multi-tenant efficiency.

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Detailed Specifications

Cyfuture Cloud deploys NVIDIA H100 GPUs optimized for AI training and inference, featuring high-bandwidth HBM3 memory to handle large language models without bottlenecks. The standard 80GB configuration supports massive datasets, with 5,120-bit memory interface and speeds up to 3.35 TB/s in SXM5 modules. For specialized needs, the NVL variant extends to 94GB, ideal for transformer-based workloads requiring extra capacity.

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Multi-Instance GPU (MIG) enables partitioning into up to 7 instances, offering flexible 10GB or 40GB slices per partition while maintaining isolation and performance. This suits inference serving or experimentation on Cyfuture's cloud servers. L2 cache at 50MB further accelerates data access.

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Cyfuture Cloud Integration

Cyfuture Cloud provides H100 GPU servers with configurable memory setups, starting from full 80GB units for enterprise AI pipelines. Users access these via on-demand rentals, supporting NVLink interconnects for multi-GPU scaling up to 600 GB/s bandwidth. Pricing aligns with 2025 market rates, emphasizing availability for Indian data centers in Delhi.[user-information] Power configurations reach 700W for peak performance without thermal throttling.

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Compared to A100 predecessors, H100 doubles bandwidth (3.35 TB/s vs. 1.6 TB/s) and introduces FP8 precision for 9x faster training. Cyfuture's infrastructure leverages TSMC 4N process for reliability in HPC environments.

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Benefits for AI Workloads

High memory capacity eliminates out-of-memory errors for models like GPT-3 equivalents, enabling seamless fine-tuning on Cyfuture platforms. Bandwidth ensures rapid data movement, critical for transformer engines processing trillion-parameter models. Fractional MIG options optimize costs for smaller teams sharing resources securely.

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In benchmarks, H100's 80GB handles 30x faster LLM GPU inference over prior generations. Cyfuture integrates this with enterprise-grade networking for hybrid cloud deployments.

Conclusion

Cyfuture Cloud's H100 GPUs standardize on 80GB HBM3 with options for 94GB NVL and MIG fractions (10GB/40GB), delivering unmatched AI performance through superior bandwidth and partitioning. These configurations position Cyfuture as a leader for scalable, high-memory GPU cloud in 2026, supporting everything from training to inference.

Follow-Up Questions

1. How does H100 memory compare to A100 or H200?

H100's 80GB HBM3 at 3.35 TB/s outperforms A100's 80GB HBM2e (2 TB/s), while H200 GPU upgrades to 141GB HBM3e for even larger models—but H100 remains more available on Cyfuture.

2. Can I rent fractional H100 memory on Cyfuture Cloud?

Yes, MIG supports 10GB or 40GB partitions across up to 7 instances per GPU, ideal for cost-effective inference.

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3. What bandwidth supports these configurations?

PCIe/SXM5 hits 3.35 TB/s; NVL reaches 3.9 TB/s; MIG maintains proportional speeds up to 3 TB/s.

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4. Is H100 available in Delhi data centers?

Cyfuture's Delhi facilities offer H100 servers with low-latency access for Indian users.

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5. What's the pricing for 80GB H100 instances?

Contact Cyfuture for 2026 quotes; market rates hover around enterprise cloud premiums, competitive with global providers.