H200 SXM on FluidStack
Visit FluidStackFluidStack's NVIDIA H200 SXM offering delivers enterprise-grade Hopper architecture GPUs with 141GB HBM3e VRAM, ideal for memory-intensive AI training, inference, and HPC workloads. As a supercloud aggregator, FluidStack unifies access to vast global resources from Tier 1-4 data centers, enabling massive-scale deployments without procurement delays. This combination stands out for ML engineers tackling large language models or multimodal AI, where H200's enhanced memory bandwidth (up to 4.8 TB/s) and compute power shine. Key value propositions include per-minute billing for cost efficiency, spot instances for up to 90% savings on interruptible workloads, and immediate capacity scaling to thousands of GPUs across regions. Target users—data scientists and AI teams—benefit from FluidStack's unified API, reducing complexity in sourcing scarce H200 inventory amid high demand. While performance consistency depends on underlying data centers, this setup prioritizes availability and flexibility over single-provider silos, making it a strategic choice for bursty, global AI projects.
Why NVIDIA H200 SXM on FluidStack?
Choose FluidStack for NVIDIA H200 SXM when needing rapid access to scarce, high-VRAM GPUs without long waitlists common at traditional providers. FluidStack's supercloud aggregates spare capacity from diverse global data centers, ensuring high availability for large clusters—perfect for H200's strengths in trillion-parameter models requiring 141GB VRAM per GPU. Per-minute billing and spot instances minimize costs for variable workloads, complementing the GPU's efficiency in memory-bound tasks. Unified interface simplifies multi-region orchestration, leveraging Hopper's FP8/FP16 precision for faster training. Unlike siloed clouds, FluidStack offers geographic diversity to mitigate latency or regulatory issues, with flexible scaling that matches H200's NVLink-enabled multi-GPU potential.
Live Pricing
Real-time NVIDIA H200 SXM offers from FluidStack
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 FluidStack | 8×NVIDIA H200 SXM 141GB VRAM | 141GB | 0 vCPU 0GB RAM | 🌍Global | $2.30/GPU/hr $18.40/hr total (8×) |
Performance Notes
On FluidStack, expect NVIDIA H200 SXM to deliver full Hopper specs: 141GB HBM3e at 4.8 TB/s bandwidth, 1,979 TFLOPS FP8 AI performance, excelling in large-model training like GPT-scale LLMs. Multi-GPU scaling via NVLink (900 GB/s) works well in clusters, but interconnects vary by data center (typically 100-400 Gbps InfiniBand/Ethernet; exact speeds unpublished). Storage options include high-IOPS NVMe, with supercloud enabling petabyte-scale distributed filesystems. Known strengths: superior memory for long-context inference. Limitations: performance may fluctuate due to aggregated infrastructure—benchmark for consistency. No public FluidStack-specific H200 benchmarks available; assume near-native NVIDIA specs with potential 5-10% overhead from virtualization.
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
141GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
FluidStack simplifies NVIDIA H200 SXM access via its intuitive dashboard and API. New users can launch GPU clusters in minutes, leveraging pre-configured images for PyTorch/TensorFlow. Focus on specifying scale, region preferences, and spot vs. on-demand for optimal cost/performance.
Steps
- 1Create a FluidStack account and add payment method via the dashboard.
- 2Navigate to GPU Marketplace, select NVIDIA H200 SXM, and choose instance size/cluster config.
- 3Configure networking, storage (e.g., attach NVMe volumes), and select spot/on-demand billing.
- 4Launch instance; access via SSH, Jupyter, or VNC from the console.
- 5Install ML frameworks and run benchmarks to verify setup.
Pro Tips
- Opt for spot instances on non-critical jobs to save up to 90%, monitoring interruptions via API alerts.
- Use FluidStack's global region selector for low-latency data proximity in multi-node training.
- Pre-warm clusters with autoscaling policies to handle H200 demand spikes efficiently.
Frequently Asked Questions
What is FluidStack's billing model for NVIDIA H200 SXM?▾
FluidStack bills per-minute for GPU instances including NVIDIA H200 SXM. Check their pricing page for the most current billing details.
Does FluidStack offer spot instances for NVIDIA H200 SXM?▾
Yes, FluidStack offers spot/preemptible instances for NVIDIA H200 SXM, 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 H200 SXM?▾
Yes, FluidStack offers reserved instance pricing for NVIDIA H200 SXM, 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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