What frameworks are supported on H100/H200?
Cyfuture AI's H100 and H200 GPU clusters support major AI frameworks including PyTorch, TensorFlow, JAX, MXNet, ONNX Runtime, along with NVIDIA libraries like CUDA, cuDNN, NCCL, TensorRT, and NeMo.?
Supported Frameworks Overview
Cyfuture AI provides high-performance NVIDIA H100 and H200 GPUs optimized for AI workloads, with full compatibility for industry-standard deep learning frameworks. These Hopper architecture GPUs leverage CUDA 12.x ecosystems, enabling seamless integration with PyTorch for flexible model training, TensorFlow for scalable production pipelines, and JAX for high-performance numerical computing.?
Additional libraries enhance capabilities: cuDNN accelerates neural network operations, NCCL optimizes multi-GPU communication across clusters, while TensorRT and NeMo support efficient inference and LLM fine-tuning. ONNX Runtime and MXNet further extend interoperability for model deployment and transfer learning.?
Cyfuture AI's GPU-as-a-Service clusters include Kubernetes orchestration with NVIDIA GPU Operator, Docker/Singularity containers, and monitoring via DCGM, Prometheus, and Grafana, ensuring frameworks run at peak efficiency on H100 (80GB HBM3) and H200 (141GB HBM3e) configurations. H100 excels in FP8-optimized transformer training via the Transformer Engine, while H200 boosts inference for large models with higher bandwidth.?
Conclusion
Cyfuture AI equips H100 and H200 GPUs with comprehensive framework support, delivering scalable performance for AI training, inference, and HPC without hardware ownership costs. Rent these clusters for flexible, enterprise-grade AI acceleration tailored to your workloads.?
Follow-up Questions & Answers
Q: Which framework is best for LLM training on Cyfuture AI's H100/H200?
A: PyTorch with NVIDIA NeMo or TensorRT-LLM offers optimal performance for LLMs, leveraging Hopper's FP8 and Transformer Engine for up to 4x faster training on H100/H200.?
Q: Does Cyfuture AI support multi-node scaling for frameworks?
A: Yes, NCCL and NVLink (up to 900GB/s) enable distributed training across 4-8 GPUs per node, scalable to 1000+ nodes via InfiniBand/Ethernet.?
Q: Are custom framework versions available?
A: Cyfuture AI provides pre-optimized Docker containers for TensorFlow, PyTorch (latest CUDA 12.x), JAX, and others; custom setups via Kubernetes are supported.?
Q: How does H200 framework support differ from H100?
A: Identical core support (PyTorch, TensorFlow, etc.), but H200's extra memory excels in large-context inference with vLLM/TGI.?
