What frameworks are supported on H100 and H200?

Cyfuture AI supports major AI frameworks on NVIDIA H100 and H200 GPUs, including PyTorch, TensorFlow, JAX, TensorRT, and CUDA. These frameworks enable seamless training, inference, and deployment of AI models on Cyfuture AI's GPU cloud infrastructure.?

Supported Frameworks Overview

H100 and H200 GPUs, powered by NVIDIA's Hopper architecture, are optimized for high-performance AI workloads at Cyfuture AI. PyTorch and TensorFlow provide flexible deep learning capabilities, with PyTorch excelling in dynamic computation graphs for research and TensorFlow offering robust production-scale deployment tools. JAX delivers high-speed numerical computing with just-in-time compilation, ideal for large-scale simulations, while TensorRT accelerates inference through optimized engine building for low-latency applications. CUDA serves as the foundational toolkit, ensuring compatibility across all frameworks and enabling custom kernel development on Cyfuture AI's H100/H200 clusters.?

Cyfuture AI's platform integrates these frameworks natively, supporting installations via pip (e.g., pip install torch torchvision torchaudio tensorflow jax) for quick setup in cloud environments. Both GPUs handle FP8 precision, Transformer Engine optimizations, and NVLink for multi-GPU scaling, making them suitable for LLMs and HPC tasks. H200's enhanced HBM3e memory (141GB vs. H100's 80GB) boosts support for memory-intensive frameworks in long-context models.?

Conclusion

Leveraging PyTorch, TensorFlow, JAX, TensorRT, and CUDA on Cyfuture AI's H100 GPU and H200 GPUs unlocks superior AI performance for training and inference. Cyfuture AI ensures backward compatibility and optimized hosting, reducing complexity for enterprise users.?

Follow-up Questions & Answers

• Q: How do I install these frameworks on Cyfuture AI's H100/H200 instances?
• A: Use standard pip commands like pip install torch tensorflow jax after launching an H100/H200 instance via Cyfuture AI's dashboard. Pre-configured Docker images with CUDA and TensorRT are also available for instant deployment.?
• Q: Are there differences in framework performance between H100 and H200?
  A: H200 excels in memory-bound tasks with frameworks like PyTorch for large LLMs due to higher bandwidth, while H100 shines in compute-heavy training across all supported frameworks.?
• Q: Does Cyfuture AI support multi-GPU configurations for these frameworks?
  A: Yes, NVLink and MIG support enable distributed training with PyTorch DDP or TensorFlow strategies on H100/H200 clusters hosted by Cyfuture AI.?
• Q: What about other frameworks like Hugging Face Transformers?
  A: Fully supported via integration with PyTorch/TensorFlow; Cyfuture AI's knowledge base provides guides for LLM deployment on H100/H200.?