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The NVIDIA H100 GPU serves as a powerhouse for high-performance computing and AI tasks. With a base clock of 1,593 MHz and a boost clock reaching 1,755 MHz, it delivers remarkable processing capabilities. Equipped with 80 GB of HBM2 memory, the H100 GPU achieves a peak bandwidth of 2,000 GB/s. This impressive combination efficiently handles extensive datasets and complex computations. Compatibility with CUDA 12.2 and NVIDIA AI Enterprise ensures top-notch performance and security for demanding applications.
Significant advancements define the NVIDIA H100 compared to its predecessors. The fourth-generation Tensor Cores provide up to 6x faster chip-to-chip communication and double the Matrix Multiply-Accumulate (MMA) rates seen with the A100. The introduction of a new FP8 data type boosts throughput by 4x. Advanced DPX Instructions accelerate dynamic programming algorithms, achieving up to 7x performance gains. The H100’s enhanced IEEE FP64 and FP32 processing, up to 3x faster, results from improved clock speeds and a higher number of Streaming Multiprocessors (SMs).
Boasting 80 GB of HBM2 memory and a 5,120-bit memory bus, the NVIDIA H100 GPU ensures robust performance. Its peak bandwidth of 2,000 GB/s facilitates the efficient management of large datasets and complex tasks. To enhance performance further, the H100 GPU features a 50 MB L2 cache that reduces memory access frequency by caching large data chunks. Additionally, the Tensor Memory Accelerator (TMA) and improved asynchronous execution capabilities contribute to more efficient data transfers and computation overlap.
The NVIDIA H100 GPU provides flexible power consumption options to meet various needs. It operates in 450 W or 600 W modes, with a maximum power limit of 400 W and a compliance limit of 310 W in the 450 W setting. In the 300 W mode, both the maximum and compliance limits are set at 310 W. This versatility allows users to balance performance and energy efficiency, accommodating diverse system requirements.
The architecture of the NVIDIA H100 GPU features advanced components that drive superior performance. It includes fourth-generation Tensor Cores, fourth-generation NVLink, and third-generation NVSwitches. This configuration supports up to 7.2 TB/sec of GPU-to-GPU throughput, significantly boosting data transfer speeds. When deploying up to 8 NVIDIA H100 GPUs in a system, the architecture provides scalable performance for intensive AI and data processing workloads.
The NVIDIA H100 GPU stands out due to its extensive core count, which is essential for high parallel processing. With 60 billion transistors, the H100’s architecture delivers substantial computational power. This makes the H100 ideal for demanding tasks in AI, data analytics, and high-performance computing.
For connectivity, the NVIDIA H100 GPU supports PCI Express Gen5 x16, Gen5 x8, and Gen4 x16 interfaces. It also accommodates NVLink bridges, enabling scalable multi-GPU setups. This flexibility enhances bandwidth and data transfer rates between GPUs, optimizing performance for complex applications.
The NVIDIA H100 GPU supports a broad range of software environments. It is compatible with Linux and Windows drivers from version R535 or later. The GPU works seamlessly with NVIDIA CUDA 12.2 or later and vGPU 16.1 or later for NVIDIA Virtual Compute Server Edition. This extensive software support ensures compatibility across various applications and virtualization environments.
Engineered for durability, the NVIDIA H100 GPU operates within a temperature range of 0°C to 50°C and can handle short-term operations between -5°C and 55°C. Its reliability extends to storage temperatures from -40°C to 75°C and various humidity tolerances. This design maintains consistent performance across diverse conditions.
Thanks to its advanced interconnect technologies, the NVIDIA H100 GPU excels in scalability. The fourth-generation NVLink provides a 3x increase in all-reduce operation bandwidth and a 50% general bandwidth boost, achieving a total bandwidth of 600 GB/sec. Third-generation NVSwitch technology enhances switch throughput to 13.6 Tbits/sec and introduces hardware acceleration for collective operations. The new NVLink Switch System allows up to 32 nodes or 256 GPUs to connect in a tapered, fat tree topology, delivering 57.6 TB/sec of all-to-all bandwidth.
The NVIDIA H100 GPU incorporates second-generation MIG technology, which offers approximately 3x more compute capacity and nearly 2x more memory bandwidth per GPU instance compared to the A100. The addition of Trusted Execution Environments (TEE) creates secure, isolated computing environments for multi-tenant operations. Native Confidential Computing support further strengthens data protection and isolation in virtualized settings.
The NVIDIA H100 GPU features several specialized capabilities designed to boost performance. The Transformer Engine accelerates transformer model training with mixed FP8 and FP16 precision, significantly enhancing AI workloads. The NVLink Switch System offers high-bandwidth, scalable connectivity across multiple servers, surpassing previous standards. Additionally, DPX Instructions enhance the performance of dynamic programming algorithms, benefiting applications such as disease diagnosis and real-time optimizations.
In data centers, the NVIDIA H100 GPU is a crucial component for both AI and HPC workloads. It delivers exceptional efficiency and performance for large-scale AI training and inference tasks, making it indispensable for data analytics and scientific research. Available in various configurations, including SMX5 and PCIe Gen 5 form factors, the H100 is featured in NVIDIA’s DGX H100 and DGX SuperPOD systems, HGX H100, and the H100 CNX Converged Accelerator, which integrates advanced networking capabilities.
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The NVIDIA H100 GPU features a memory footprint of 94 GB of HBM3 memory, supported by a 6,016-bit memory bus and offering peak bandwidth of 3,938 GB/s. The cost of the NVIDIA H100 GPU varies by vendor and configuration, but it generally starts around $30,000. Exact prices may differ based on purchase agreements and volume. The NVIDIA H100’s high cost is due to its advanced technology, including cutting-edge Tensor Cores, high bandwidth, and large memory capacity. It targets high-performance computing and AI applications, justifying its premium price. Yes, the NVIDIA H100 is available through various NVIDIA partners and resellers. Availability may vary based on demand and geographic region. Yes, the H100 outperforms the A100 with significant improvements in performance and efficiency. It features enhanced Tensor Cores, increased memory bandwidth, and new data types, leading to faster and more efficient processing. For organizations needing top-tier AI and HPC performance, the H100 offers substantial benefits. Its advanced features and capabilities justify the investment for demanding tasks and workloads. The NVIDIA H100 remains a top-tier GPU for AI and high-performance computing, but the newer H200 and Blackwell B200 have more advanced features. These models offer improved performance efficiency and architectural enhancements. While the H200 and B200 may surpass the H100 in some areas, the H100 still holds strong in many HPC and AI workloads. The NVIDIA A100 GPU is considered the predecessor and equivalent in the context of earlier high-performance GPUs. While the H100 represents a significant advancement over the A100, they are often compared as they are both designed for similar high-performance computing and AI tasks. The NVIDIA H100 GPU does not include RT Cores. Instead, it focuses on advanced Tensor Cores for AI and high-performance computing tasks. The H100 is designed to deliver exceptional throughput and performance for AI workloads, data analytics, and HPC applications, but it does not support real-time ray tracing, which is typically managed by RT Cores in NVIDIA’s RTX series GPUs. The NVIDIA H100 GPU features 80 billion transistors and includes 72 Compute Units. The NVIDIA H100 GPU is used for high-performance computing, artificial intelligence, and data analytics. It excels in training large AI models, running complex simulations, and processing vast datasets. The successor to the NVIDIA H100 GPU is the Blackwell B200. This new architecture represents the next major step after the Hopper (H100) generation, offering significant improvements in AI workloads, deep learning, and accelerated computing performance. The B200 continues NVIDIA’s focus on advancing GPU technology for data centers, AI research, and high-performance computing. The H100 can be up to 6x faster in chip-to-chip communication and 3x faster in IEEE FP64 and FP32 processing compared to the A100, thanks to advanced architecture and features. The H100 and H200 GPUs differ mainly in their architecture and performance features. The H200 is a newer model with enhancements beyond the H100, such as improved data transfer rates and processing power. The NVIDIA Blackwell architecture is the most powerful GPU for data centers, offering superior performance for AI, machine learning, and high-performance computing. With advanced compute density, Multi-Instance GPU (MIG) technology, and fast NVLink interconnects, it handles large-scale workloads efficiently. Blackwell’s energy-efficient design enhances scalability and performance, making it ideal for data centers seeking maximum efficiency and reduced operational costs. NVIDIA released the H100 GPU in the second half of 2022, marking its latest advancement in high-performance computing and AI technology. The NVIDIA H100 GPU includes 94 GB of HBM3 memory, providing substantial RAM for handling large datasets and complex computations. The NVIDIA H100 is extremely powerful, featuring up to 6x faster chip-to-chip communication and up to 7x performance gains in dynamic programming algorithms compared to its predecessors. The H100 is designed for professional AI and high-performance computing, whereas the RTX 4090 targets gaming and creative tasks. For AI and data workloads, the H100 is superior in performance. The NVIDIA H100 GPU does not use a CPU. It is a standalone GPU designed for high-performance computing and AI workloads. It operates independently of the CPU and is used in conjunction with various CPUs in a system to accelerate tasks such as machine learning, data analysis, and other intensive computations.
