NVIDIA H800

Description

Detailed Parameter Table

NVIDIA H800

NVIDIA H800

Product Introduction

NVIDIA H100

The NVIDIA H100 is NVIDIA’s flagship data center GPU, engineered to redefine performance for AI training, inference, and high-performance computing (HPC) workloads. As the cornerstone of NVIDIA’s Hopper architecture lineup, the NVIDIA H100 bridges the gap between general-purpose computing and specialized AI acceleration—delivering unmatched throughput for large language models (LLMs), scientific simulations, and real-time data analytics. Unlike its successor, the NVIDIA GH200 (which integrates additional system-level features), the NVIDIA H100 focuses on GPU-centric performance, making it the go-to choice for organizations building scalable, GPU-dense data centers.

In modern AI infrastructure, the NVIDIA H100 acts as a “performance anchor”: its 16,896 CUDA Cores and 528 4th Gen Tensor Cores handle the compute-intensive demands of LLM training (e.g., GPT-3, PaLM), while its 80GB HBM3 memory eliminates bottlenecks when processing terabyte-scale datasets. For example, a cloud service provider using the NVIDIA H100 can train a 175B-parameter LLM in weeks (vs. months with prior-gen GPUs), drastically reducing time-to-market for AI services. In HPC, the NVIDIA H100 accelerates simulations like quantum chemistry and climate modeling—delivering 67 TFLOPS of FP64 performance to tackle complex scientific challenges. Today, the NVIDIA H100 remains a staple in data centers worldwide, trusted by enterprises, research labs, and cloud providers to power the next generation of AI and HPC innovations.

NVIDIA H800

The NVIDIA H800 is a data center GPU designed to meet the demands of AI and HPC workloads, particularly in regions where specific requirements or regulations are in place. Built on the Hopper architecture, it offers a balance between performance and cost – effectiveness. While sharing the same 4nm manufacturing process as the H100, the H800 has a slightly different core configuration, with 14,592 CUDA cores and 456 tensor cores. This setup makes it well – suited for inference tasks and medium – scale training, such as those encountered in enterprise – level AI applications like customer service chatbots or fraud detection systems.

The H800 comes with 80GB of HBM2e memory, providing a memory bandwidth of 2,039 GB/s. Although not as high as the H100’s HBM3 memory bandwidth, it still offers substantial capacity for handling large datasets. Its reduced NVLink bandwidth (compared to the H100) limits its multi – GPU scalability to some extent but makes it more suitable for scenarios where a smaller number of GPUs are required, or where network – based multi – GPU communication is sufficient. With a TDP of 350W, the H800 is more power – efficient in terms of raw power consumption, making it easier to deploy in servers with limited power budgets.

Core Advantages and Technical Highlights

NVIDIA H100

4th Gen Tensor Cores for Breakthrough AI Performance: The NVIDIA H100’s 4th Gen Tensor Cores introduce FP8 precision—delivering 3,291 TFLOPS of AI training performance (2x faster than the prior – gen A100). This precision balances speed and accuracy, critical for training LLMs and computer vision models. For a retail company using the NVIDIA H100 to train a product recommendation model, FP8 reduces training time by 50% while maintaining 99% of the accuracy achieved with higher – precision FP16. This efficiency lets teams iterate on models faster, improving recommendation relevance and customer engagement.

80GB HBM3 Memory for Large – Scale Workloads: Unlike GPUs with smaller memory pools (e.g., 40GB HBM2e), the NVIDIA H100’s 80GB HBM3 memory (with 3.35 TB/s bandwidth) enables end – to – end processing of large datasets without offloading to slower system memory. In a pharmaceutical research lab, the NVIDIA H100 can run molecular dynamics simulations on a 100M – atom protein structure—keeping all data in HBM3 to avoid 10x slowdowns associated with memory swapping. This capability is a game – changer for workloads where data locality directly impacts time – to – result.

NVLink 4.0 for Scalable Multi – GPU Clusters: The NVIDIA H100’s 4x NVLink 4.0 ports (400 GB/s per link) enable seamless connectivity between up to 8 NVIDIA H100 GPUs, forming a “GPU supercomputer” for distributed workloads. A university research team using 8 NVIDIA H100 GPUs linked via NVLink can run a climate simulation 8x faster than a single GPU setup—cutting the time to model a 10 – year weather pattern from 8 weeks to 1 week. NVLink also simplifies cluster management, as GPUs share data directly (without relying on network switches), reducing latency and complexity.

Power Efficiency for Sustainable Data Centers: Despite its high performance, the NVIDIA H100 delivers 1.4x more performance per watt than the prior – gen A100. Its 700W TDP is optimized for dense server deployments—allowing 4 NVIDIA H100 GPUs to fit in a single 4U server (vs. 2 GPUs with less efficient models). For a hyperscaler operating a 10,000 – GPU data center, the NVIDIA H100 reduces annual power costs by \(1.2M (based on \)0.10/kWh), aligning with sustainability goals while maximizing compute density.

NVIDIA H800

Cost – Effective AI and HPC Processing: The H800 offers a cost – effective solution for organizations looking to enter the realm of AI and HPC without the high cost associated with top – tier GPUs like the H100. Its performance is well – tuned for medium – scale AI tasks, such as training smaller – sized language models or running real – time inference for business – critical applications. For a small – to – medium – sized enterprise (SME) in the e – commerce industry, the H800 can be used to train product image recognition models for inventory management, providing a significant performance boost over traditional CPUs at a more affordable price point.

Simplified Deployment with Lower Power Requirements: With a TDP of 350W, the H800 is easier to integrate into existing server infrastructures that may have power limitations. Its passive cooling option (in some models) further simplifies deployment, as it reduces the need for complex and noisy active cooling systems. This makes it an attractive choice for edge data centers or small – scale data center deployments where space and power management are crucial. For example, an edge computing facility in a remote area can use the H800 to perform real – time video analytics for security monitoring, without the need for a large – scale power upgrade.

Compatibility with AI and HPC Ecosystems: The H800 is compatible with a wide range of software in the AI and HPC ecosystems, including CUDA – based applications. This ensures that organizations can leverage existing codebases and development tools to accelerate their workflows. For instance, a research group using open – source AI frameworks like PyTorch or TensorFlow can easily adapt their code to run on the H800, taking advantage of its tensor cores for faster AI computations.

NVIDIA H800

Typical Application Scenarios

NVIDIA H100

Large Language Model (LLM) Training: Cloud providers like AWS and Google Cloud use the NVIDIA H100 to power their AI – as – a – service offerings. For example, AWS’s P5 instances (equipped with 8 NVIDIA H100 GPUs) let customers train 175B – parameter LLMs in ~20 days—down from 60 days with A100 – based instances. The NVIDIA H100’s FP8 precision and HBM3 memory ensure that even the largest models run efficiently, enabling customers to build custom LLMs for chatbots, content generation, and code assistance.

Quantum Chemistry Simulations: Research labs like MIT’s Department of Chemistry use the NVIDIA H100 to accelerate quantum chemistry calculations. The GPU’s 67 TFLOPS of FP64 performance lets scientists model the behavior of complex molecules (e.g., drug candidates) in hours—vs. days with CPU – only systems. For a team developing a new cancer treatment, the NVIDIA H100 reduces the time to simulate a molecule’s interaction with a protein from 48 hours to 6 hours, speeding up drug discovery timelines.

Real – Time AI Inference for Edge Clouds: Telecommunication companies use the NVIDIA H100 in edge data centers to run low – latency AI inference. For example, a 5G provider deploying the NVIDIA H100 can process 1M video streams per GPU (for real – time object detection in smart cities) with sub – 10ms latency. The GPU’s TensorRT optimization further boosts inference throughput—ensuring that critical applications like traffic management and public safety run without delays.

NVIDIA H800

Enterprise – Level AI Inference: In large enterprises, the H800 can be used for real – time AI inference in areas such as customer service chatbots, fraud detection, and recommendation engines. For a financial institution, the H800 can analyze thousands of transactions per second to detect fraudulent activities in real – time. Its ability to handle large volumes of data quickly and efficiently makes it a valuable asset for maintaining the security and integrity of financial operations.

Medium – Scale AI Model Training: Smaller research teams or companies with limited resources can use the H800 to train medium – sized AI models. For example, a startup in the natural language processing field can use the H800 to train a custom language model for sentiment analysis of social media data. The H800 provides enough computing power to train these models in a reasonable time frame, while keeping costs manageable.

Edge – Based AI Computing: Given its lower power consumption and passive cooling option, the H800 is suitable for edge – based AI computing scenarios. For example, in smart retail stores, the H800 can be used in edge devices to analyze customer behavior in real – time, such as tracking foot traffic patterns and customer dwell times. This data can then be used to optimize store layout and product placement.

Related Model Recommendations

NVIDIA H100

NVIDIA GH200: Successor System – on – Chip (SoC). The NVIDIA GH200 integrates the H100 GPU with additional CPU and memory, ideal for workloads needing unified system memory (e.g., large – scale AI training with multi – terabyte datasets). It replaces the NVIDIA H100 in scenarios where system – level integration is prioritized over GPU – only performance.

NVIDIA A100: Prior – Gen Alternative. The NVIDIA A100 (Ampere architecture) offers 1,950 TFLOPS of AI performance—30% less than the NVIDIA H100—at a lower cost. It’s a cost – effective choice for small – to – medium enterprises (SMEs) training smaller models (e.g., 10B – parameter LLMs) or running basic HPC workloads.

NVIDIA H100 PCIe: Low – Power Variant. The NVIDIA H100 PCIe (350W TDP) is a reduced – power version of the NVIDIA H100, designed for servers with limited power budgets (e.g., edge data centers). It retains 80% of the full – power H100’s performance, making it suitable for inference – heavy workloads.

NVIDIA DGX H100: Turnkey AI System. The NVIDIA DGX H100 is a pre – configured server with 8 NVIDIA H100 GPUs, NVLink 4.0, and NVIDIA AI Enterprise software. It eliminates the complexity of building a custom GPU cluster, ideal for research labs and enterprises new to large – scale AI.

Dell PowerEdge R760xa: H100 – Optimized Server. The Dell PowerEdge R760xa supports up to 4 NVIDIA H100 GPUs, with redundant power supplies and liquid cooling to handle the GPU’s 700W TDP. It’s a reliable choice for enterprises deploying the NVIDIA H100 in production data centers.

NVIDIA Quantum – 2 InfiniBand Switch: Networking Complement. The NVIDIA Quantum – 2 switch (400 Gb/s per port) works with the NVIDIA H100 to build multi – node GPU clusters. It reduces network latency by 50% vs. Ethernet switches, critical for distributed LLM training.

PyTorch 2.0: AI Framework Optimization. PyTorch 2.0 includes compiler optimizations for the NVIDIA H100’s Hopper architecture, delivering 30% faster training for LLMs. It’s the de facto framework for teams using the NVIDIA H100 to build custom AI models.

NVIDIA AI Enterprise 5.0: Software Suite. NVIDIA AI Enterprise 5.0 provides enterprise – grade support for the NVIDIA H100, including pre – trained models, security patches, and 24/7 technical assistance. It’s essential for enterprises deploying the NVIDIA H100 in regulated industries (e.g., healthcare, finance).

NVIDIA H800

NVIDIA H100: Higher – Performance Counterpart. The NVIDIA H100 offers significantly higher performance in terms of compute power, memory bandwidth, and NVLink capabilities. It is suitable for organizations that require the absolute best in AI training and HPC performance, such as large – scale cloud providers and top – tier research institutions. However, it comes at a higher cost and with higher power requirements.

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