H200 GPU Rental

H200 features 141GB of HBM3e memory compared to H100's 80GB HBM3 (1.76x more capacity), and 4.8 TB/s memory bandwidth vs 3.35 TB/s (1.43x faster). The core compute capabilities are identical, but the increased memory makes H200 ideal for inference workloads, especially for large language models. You can fit larger models, run bigger batch sizes, or serve multiple models concurrently.

H200 excels at both, but it's particularly advantageous for inference. The extra memory allows larger batch sizes for better throughput, multiple model serving, and longer context windows for LLMs. For training workloads where memory isn't the bottleneck, H100 might offer better price-performance. Think of H200 as the 'inference-optimized' variant of H100.

Absolutely! With 141GB of memory, you can serve multiple models concurrently. For example: 2-3 models in the 20-30B parameter range, or 3-5 smaller models (7-13B). This is perfect for applications that need different specialized models (e.g., general chat + code generation + summarization) or A/B testing different model versions.

Single H200 can handle: up to 70B parameters comfortably with good batch sizes (e.g., LLaMA 2 70B, Mixtral 8x7B), and up to 100B parameters with smaller batches. For models above 100B, you'll want to use tensor parallelism across 2-4 H200s. The extra memory vs H100 means you can run larger batches, which translates to better throughput and lower cost per token.

H200 is excellent for RAG (Retrieval-Augmented Generation) because you can load both the LLM and embedding models in GPU memory, plus cache frequently accessed embeddings. The 141GB capacity allows you to keep large knowledge bases in memory alongside your language model, reducing latency. This is particularly beneficial for enterprise knowledge systems with millions of documents.

All major inference frameworks support H200: TensorRT-LLM (highest performance, NVIDIA official), vLLM (excellent for OpenAI-compatible serving), Text Generation Inference (HuggingFace, easy integration), and DeepSpeed Inference. All frameworks are optimized to leverage the increased memory and bandwidth of H200, especially for large batch inference.

Yes! H200 is great for fine-tuning, especially with larger batch sizes. The extra memory allows LoRA/QLoRA fine-tuning of larger models (70B+) or standard fine-tuning with bigger batches for faster convergence. For full fine-tuning of very large models (100B+), you may want to use multiple H200s with data/model parallelism.

H200 is ideal for: production LLM inference (30B-100B parameter models), multi-model serving, RAG systems with large knowledge bases, high-throughput chatbots, and fine-tuning large models. If you're deploying LLMs at scale and memory is a constraint with H100, H200 is your best choice. For training-heavy workloads or smaller models, H100 or A100 might be more cost-effective.

H200 instances are provisioned in 60-90 seconds on Spheron. Our infrastructure is optimized for rapid deployment with pre-warmed GPU pools. You can deploy, load your model, and start serving inference requests in under 5 minutes total. We also support saved snapshots for even faster redeployment of configured environments.

Yes! We support H200 clusters with 2, 4, or 8 GPUs connected via NVLink switch for tensor parallel inference. This is useful for models larger than 141GB or when you need extreme throughput. An 8x H200 cluster provides 1.1TB of unified GPU memory and can serve 200B+ parameter models or handle massive concurrent inference load. Contact our team for cluster pricing.

Yes, Spheron offers Spot instances for H200 at significantly reduced rates (up to 70% savings). However, Spot instances can be interrupted when demand increases. Key risks include: potential job interruption during training/inference, loss of unsaved state or checkpoints, and need to restart from last saved checkpoint. Best practices: implement frequent checkpointing (every 15-30 minutes), use Spot for fault-tolerant workloads, save model weights to persistent storage regularly, and consider Spot for development/testing rather than production inference. For critical production workloads, we recommend dedicated instances with SLA guarantees.