H100 SXM5 on FluidStack
Visit FluidStackFluidStack's NVIDIA H100 SXM5 offering combines the powerhouse Hopper architecture GPU—boasting 80GB HBM3 VRAM—with a supercloud aggregator model that unifies access to vast global GPU resources. This setup is noteworthy for enabling massive-scale AI and ML training runs, where immediate access to thousands of H100s across Tier 1-4 data centers is critical. Target audiences include ML engineers and data scientists at enterprises tackling large language models, distributed training, or HPC workloads requiring rapid scaling without procurement delays. Key value propositions include per-minute billing for cost efficiency, spot instances leveraging spare capacity for up to 70% savings, and a unified API/interface for seamless orchestration. FluidStack's global footprint minimizes latency for multi-region deployments, while the H100 SXM5 delivers up to 4x faster inference and 9x training throughput over A100s on benchmarks like MLPerf. Ideal for bursty, high-demand workloads where traditional providers fall short on availability.
Why NVIDIA H100 SXM5 on FluidStack?
Choose FluidStack for NVIDIA H100 SXM5 when needing on-demand access to enterprise-grade GPUs at scale, without the limitations of single-provider capacity constraints. FluidStack's supercloud aggregates spare capacity from diverse data centers worldwide, ensuring high availability for H100s that are often backlogged elsewhere. This complements the H100's strengths in transformer-based models and FP8 precision training via its Transformer Engine. Per-minute billing and spot instances optimize costs for variable workloads, potentially slashing expenses versus on-demand rates. Global reach supports low-latency multi-region clusters, and the unified interface simplifies management across heterogeneous infrastructure—perfect for teams prioritizing speed-to-results over vendor lock-in.
Live Pricing
Real-time NVIDIA H100 SXM5 offers from FluidStack
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 FluidStack | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 0 vCPU 0GB RAM | 🌍Global | $2.10/GPU/hr $16.80/hr total (8×) |
Performance Notes
On FluidStack, expect H100 SXM5 to deliver peak Hopper performance: up to 4 petaFLOPS FP8 AI compute, 80GB HBM3 at 3.35 TB/s bandwidth. Networking typically includes 400 Gbps InfiniBand or RoCE in clustered configs, enabling efficient multi-node scaling for DGX-like setups. Storage options feature NVMe SSDs with 10-100 GB/s throughput. Multi-GPU scaling via NVLink (900 GB/s) shines in pod configurations up to 256 GPUs. Real-world MLPerf results show strong training/inference gains, but exact FluidStack benchmarks are sparse—performance varies by data center tier and spot availability. Test small-scale first to validate interconnect consistency.
A supercloud aggregator providing a unified interface to vast GPU resources from global data centers.
Best For
Unique Features
- Supercloud architecture pooling global resources
- Aggregation of spare capacity from Tier 1-4 data centers
VRAM
80GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Getting started with FluidStack's H100 SXM5 is straightforward via their intuitive dashboard or API. Sign up for instant access to global inventory, select configurations, and launch clusters in minutes—ideal for rapid prototyping or scaling ML workloads without hardware commitments.
Steps
- 1Create a free FluidStack account and add payment method for per-minute billing.
- 2Navigate to GPU marketplace, filter for H100 SXM5, and select region/quantity.
- 3Choose instance type (on-demand/spot), OS/image (e.g., Ubuntu with CUDA 12+), and storage.
- 4Configure networking (public IP, VPC) and launch—clusters deploy in under 5 minutes.
- 5SSH/connect via dashboard and install frameworks like PyTorch for immediate use.
Pro Tips
- Opt for spot instances to save 50-70% on large training runs, with auto-fallback to on-demand.
- Leverage FluidStack's API for autoscaling clusters based on queue depth in tools like Ray.
- Monitor utilization via Prometheus integration to optimize H100's FP8/Transformer Engine features.
Frequently Asked Questions
What is FluidStack's billing model for NVIDIA H100 SXM5?▾
FluidStack bills per-minute for GPU instances including NVIDIA H100 SXM5. Check their pricing page for the most current billing details.
Does FluidStack offer spot instances for NVIDIA H100 SXM5?▾
Yes, FluidStack offers spot/preemptible instances for NVIDIA H100 SXM5, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and training jobs with checkpointing. Note that spot instances can be interrupted when demand is high, so ensure your workflow can handle preemption gracefully.
Does FluidStack offer reserved instances for NVIDIA H100 SXM5?▾
Yes, FluidStack offers reserved instance pricing for NVIDIA H100 SXM5, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact FluidStack for current reserved pricing and commitment terms.
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