H200 GPU Rental
From $1.56/hr - Enhanced H100 with 141GB HBM3e for LLM Inference
The NVIDIA H200 Tensor Core GPU is the enhanced version of the industry-leading H100, featuring 141GB of faster HBM3e memory and improved bandwidth. Built on the proven Hopper architecture, the H200 delivers 1.4x more memory capacity and 1.2x higher bandwidth than H100, making it ideal for serving large language models and memory-intensive AI inference workloads. Get superior price-performance for LLM deployment on Spheron's infrastructure.
Technical Specifications
Ideal Use Cases
Large Language Model Inference
Deploy and serve LLMs up to 100B parameters with exceptional throughput and low latency, leveraging 141GB memory for larger batch sizes.
- •ChatGPT-scale inference serving millions of users
- •Enterprise chatbots with long context windows (32K+ tokens)
- •Multi-turn conversations with extended memory
- •Real-time code generation and completion services
High-Throughput AI Inference
Maximize inference throughput for production workloads with increased memory bandwidth and capacity for concurrent model serving.
- •Multi-model serving with dynamic batching
- •Real-time recommendation systems at scale
- •Computer vision inference for video analytics
- •Voice assistant and speech recognition services
RAG & Knowledge Systems
Power retrieval-augmented generation systems that require loading large knowledge bases alongside LLMs in GPU memory.
- •Enterprise knowledge bases with LLM integration
- •Document analysis and Q&A systems
- •Legal and medical AI assistants
- •Multi-document reasoning and synthesis
LLM Fine-Tuning & Adaptation
Fine-tune and adapt pre-trained models for specific domains with larger batch sizes enabled by expanded memory.
- •Domain-specific model fine-tuning (legal, medical, finance)
- •Instruction tuning for custom behaviors
- •RLHF (Reinforcement Learning from Human Feedback)
- •LoRA and QLoRA efficient fine-tuning
Pricing Comparison
| Provider | Price/hr | Savings |
|---|---|---|
SpheronBest Value | $1.56/hr | - |
Lambda Labs | $4.99/hr | 3.2x more expensive |
CoreWeave | $5.25/hr | 3.4x more expensive |
Azure | $8.50/hr | 5.4x more expensive |
AWS | $8.75/hr | 5.6x more expensive |
Google Cloud | $13.20/hr | 8.5x more expensive |
Performance Benchmarks
Frequently Asked Questions
How is H200 different from H100?
H200 features 1.76x more memory (141GB vs 80GB) and 1.4x higher bandwidth (4.8 TB/s vs 3.35 TB/s) compared to H100. While compute performance is similar, the expanded memory makes H200 ideal for inference workloads requiring larger context windows and batch sizes, particularly for LLMs and RAG applications.
Is H200 better for training or inference?
H200 excels at inference workloads, especially for large language models. The expanded memory allows serving larger models with bigger batch sizes and longer context windows. While it can handle training, H100 often provides better cost-performance for training workloads. Choose H200 when memory capacity is your bottleneck.
Can I fit larger models in H200's 141GB memory?
Yes! H200's 141GB allows you to serve models like LLaMA 2 70B, GPT-3 175B (with optimizations), or multiple smaller models concurrently. With techniques like quantization (8-bit/4-bit) and memory optimization, you can serve even larger models. The extra memory also enables longer context windows (32K+ tokens) without performance degradation.
Does H200 support multi-GPU configurations?
Yes! Spheron supports multi-GPU H200 configurations for massive inference throughput or training of extremely large models. However, availability may be limited as H200 is newer. Contact our team for multi-GPU H200 deployments and custom pricing. Book a call with our team
What's the minimum rental period for H200?
No minimum! Spheron offers per-minute billing for H200 instances. Test your inference workload for an hour or run production services for months. Pay only for actual usage with no long-term commitments required.
Ready to Get Started with H200?
Deploy your H200 GPU instance in minutes. No contracts, no commitments. Pay only for what you use.
