Everything you need to know about NVIDIA H100 SXM

Interface: NVLink
Power Consumption: Up to 700W
Memory: 80GB HBM3
Memory Bandwidth: 2.0 TB/s
Cooling Design: Advanced liquid cooling solutions typically required, given the high power consumption.
Form Factor: SXM5, a custom form factor for high-performance computing environments.
Architecture: NVIDIA Hopper
Compatibility: Designed for specialized server environments with NVLink support, not compatible with standard PCIe slots.
Compute Cores: 16,896 CUDA cores and 528 Tensor cores.
Compute Performance: Up to 60 TFLOPS of FP64, 120 TFLOPS of TF32, and 1000 TFLOPS of FP8 performance.
MIG Technology: Supports partitioning into up to seven independent instances, offering enhanced flexibility and efficiency.
Special Features: Includes a dedicated Transformer Engine for large language models.
Confidential Computing: Supports data encryption during processing.

AI and Deep Learning: The H100 SXM accelerates AI training and inference, particularly for large language models, significantly reducing development time.
High-Performance Computing (HPC): Ideal for complex simulations like climate modeling and genomic sequencing, providing the computational power needed for rapid scientific advancements.
Data Analytics: Enables real-time analysis of large datasets, crucial for quick, data-driven decisions across various industries.
Graphics Rendering: Supports advanced graphics rendering, improving the quality and reducing the time required for 3D models and virtual environments.

Cooling and Power: Requires up to 700W and advanced cooling (liquid cooling recommended) to manage heat output effectively.
Infrastructure Compatibility: Best deployed in systems supporting the SXM5 form factor and NVLink, such as NVIDIA DGX servers, for optimal performance in multi-GPU setups.
Software Optimization: Leverage NVIDIA's software tools (CUDA, cuDNN, TensorRT) to fully utilize the H100's Tensor Cores and Transformer Engine.
Scalability: Use NVLink for connecting multiple H100 GPUs to ensure efficient scaling in large AI models and complex simulations with low-latency data transfer.

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