H200 SXM on JarvisLabs
Visit JarvisLabsJarvisLabs brings the cutting-edge NVIDIA H200 SXM GPU to developers, students, and hobbyists with its hallmark simplicity for AI workloads. Featuring 141GB of HBM3e memory and 4.8 TB/s bandwidth on the Hopper architecture, the H200 SXM is engineered for enterprise-grade tasks like training massive LLMs (100B+ parameters), high-resolution generative AI, and complex simulations—surpassing the H100's 80GB capacity by 1.75x. This combination stands out for making premium compute accessible via one-click Jupyter environments, per-minute billing, spot instances, and a unique pause feature that stops GPU charges while preserving storage and session state. Ideal for fast.ai learners and cost-conscious experimenters, it democratizes large-scale ML without the overhead of enterprise clouds. Key value propositions include rapid setup, flexible pricing for intermittent use, and reliable performance, empowering ML engineers to prototype efficiently on hardware previously reserved for big tech.
Why NVIDIA H200 SXM on JarvisLabs?
JarvisLabs pairs perfectly with the NVIDIA H200 SXM by aligning its developer-focused simplicity with the GPU's memory-intensive prowess. Unique advantages include the pause functionality, allowing users to halt billing on costly H200 compute mid-session while retaining Jupyter notebooks and data—ideal for students pausing overnight. Per-minute billing and spot instances offer up to 70% savings for bursty experimentation, unlike rigid hourly enterprise plans. One-click Jupyter launches with pre-installed ML frameworks (PyTorch, TensorFlow) minimize setup friction, letting users focus on models exploiting 141GB VRAM for long-context inference or fine-tuning. This combo excels for hobbyists and fast.ai users needing high-end Hopper power without DevOps complexity, providing scalable, affordable access to enterprise-tier capabilities.
Live Pricing
Real-time NVIDIA H200 SXM offers from JarvisLabs
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
JarvisLabs | NVIDIA H200 SXM 141GB VRAM | 141GB | 16 vCPU 141GB RAM | 🌍Global | $3.80/GPU/hr |
Performance Notes
The NVIDIA H200 SXM on JarvisLabs delivers Hopper architecture peaks: 141GB HBM3e at 4.8 TB/s bandwidth enables 1.9x faster large-model training/inference vs. H100, with exceptional FP8/FP16 throughput for LLMs. Network bandwidth details are provider-specific but support multi-GPU via NVLink for near-linear scaling in 2-8 GPU configs; InfiniBand likely enables cluster training. Fast NVMe storage handles large datasets effectively. JarvisLabs reports strong benchmarks for fine-tuning (e.g., Llama 70B viable), though full public H200 metrics are emerging—expect 20-30% uplifts over H100 in memory-bound tasks. Unknowns include exact interconnect latency; test with NCCL benchmarks. Honest perf aligns with DGX standards, optimized for AI/HPC.
A developer and hobbyist-focused provider emphasizing extreme simplicity for AI workloads.
Best For
Unique Features
- Pause functionality to stop compute billing while preserving storage
- One-click Jupyter environments
VRAM
141GB
Architecture
Hopper
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA H200 SXM on JarvisLabs is designed for instant productivity: sign up, select the GPU in their intuitive dashboard, and spin up a pre-configured Jupyter instance in minutes. Per-minute billing and pause features ensure cost control from the start, perfect for experimenting with massive models without setup hurdles.
Steps
- 1Sign up for a free JarvisLabs account at jarvislabs.ai and add payment details.
- 2Click 'Create Instance', select NVIDIA H200 SXM, and pick Jupyter or custom image.
- 3Configure storage (NVMe SSD), RAM/CPU, and billing type (on-demand or spot).
- 4Hit 'Launch'—access JupyterLab or SSH within 2-5 minutes via provided link.
- 5Upload datasets, install deps if needed, and run workloads with CUDA 12.x pre-loaded.
Pro Tips
- Pause instances right after use to stop GPU billing while keeping storage and notebooks intact for seamless resumption.
- Choose spot instances for 50-70% cost savings on non-critical experiments, monitoring for interruptions.
- Pre-warm large datasets to NVMe and use DeepSpeed/ZeRO for optimal multi-GPU scaling on 141GB VRAM.
Frequently Asked Questions
What is JarvisLabs's billing model for NVIDIA H200 SXM?▾
JarvisLabs bills per-minute for GPU instances including NVIDIA H200 SXM. Check their pricing page for the most current billing details.
Does JarvisLabs offer spot instances for NVIDIA H200 SXM?▾
Yes, JarvisLabs 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.
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