NVIDIA H100 vs. GH200: Choosing the Right GPU for Your AI Workloads

Introduction

AI and High-Performance Computing (HPC) workloads are becoming increasingly demanding, driven by larger models, higher throughput requirements, and more complex data pipelines. As a result, hardware choices must account not only for raw compute performance, but also for memory capacity, bandwidth, and system-level efficiency. NVIDIA’s accelerators play a central role in meeting these demands, powering workloads ranging from scientific simulations to large language model (LLM) training and inference.

Within NVIDIA’s Hopper generation, two closely related platforms stand out: the H100 Tensor Core GPU and the GH200 Grace Hopper Superchip. The H100, introduced in 2022, represents a major leap in GPU compute performance and efficiency for AI workloads. The GH200 builds on the H100 by pairing it with a Grace CPU and a unified memory architecture, targeting workloads where memory size and CPU-GPU communication become limiting factors.

This blog provides a detailed comparison of the NVIDIA H100 and GH200, covering their architectural differences, core system characteristics, performance behavior, and best-fit applications to help you choose the right platform for your AI and HPC workloads.

Overview of H100 & GH200 GPUs

NVIDIA H100 (Hopper GPU)

The H100 is NVIDIA’s data-center GPU designed for large-scale AI and HPC workloads. It introduces fourth-generation Tensor Cores and the Transformer Engine with FP8 support, enabling higher throughput and better efficiency for transformer-based models.

Key characteristics:

• Hopper architecture GPU
  
  
• 80GB HBM3 memory
  
  
• High memory bandwidth
  
  
• NVLink support for multi-GPU scaling
  
  
• Available in PCIe and SXM form factors

The H100 is a general-purpose accelerator intended to handle a wide range of training and inference workloads efficiently.

NVIDIA GH200 (Grace Hopper Superchip)

The GH200 is not a standalone GPU. It is a system-level design that tightly couples an H100 GPU with an NVIDIA Grace CPU using NVLink-C2C. The defining feature of GH200 is its unified memory architecture, where the CPU and GPU share access to a large, coherent memory pool.

Key characteristics:

• Grace CPU + H100 GPU in a single package
• Up to hundreds of gigabytes of shared memory
• High-bandwidth, low-latency CPU-GPU interconnect
• Designed for tightly coupled, memory-intensive workloads

GH200 targets scenarios where system architecture and data movement are limiting factors rather than raw GPU compute.

Architectural Evolution: Hopper GPU to Grace Hopper Superchip

While both H100 and GH200 are based on NVIDIA’s Hopper architecture, they represent different levels of system design. The H100 focuses on GPU-centric acceleration, whereas GH200 expands the scope to CPU-GPU integration.

NVIDIA H100 (Hopper Architecture)

Launched in 2022, the H100 Tensor Core GPU was designed to meet the needs of large-scale AI workloads, particularly transformer-based models. Built on a 5 nm process with roughly 80 billion transistors, the H100 introduces several architectural advancements aimed at improving both performance and efficiency.

Key innovations include fourth-generation Tensor Cores and the Transformer Engine, which supports FP8 precision. This allows faster training and inference for large models while maintaining accuracy. The H100 also introduces DPX instructions to accelerate dynamic programming workloads, along with Distributed Shared Memory and Thread Block Clusters to improve execution efficiency across Streaming Multiprocessors (SMs).

The second-generation Multi-Instance GPU (MIG) architecture improves workload isolation by increasing compute capacity and memory per instance. Confidential Computing support adds secure execution environments for sensitive workloads. Together, these changes make the H100 a purpose-built accelerator for modern AI and HPC applications.

NVIDIA GH200 (Grace Hopper Architecture)

The GH200 extends the Hopper GPU into a system-level design by tightly coupling an H100 GPU with an NVIDIA Grace CPU. Rather than relying on traditional PCIe connections, GH200 uses NVLink-C2C, a high-bandwidth, coherent interconnect that allows the CPU and GPU to share a unified memory space.

This architecture fundamentally changes how data moves through the system. CPU and GPU memory are accessible without explicit copies, reducing latency and simplifying memory management. GH200 is designed for workloads where memory capacity, CPU preprocessing, or frequent CPU–GPU synchronization limit performance more than raw GPU compute.

Architectural Differences (H100 vs GH200)

Core Specifications: A System-Level Comparison

Examining specifications highlights how GH200 extends H100 beyond the GPU itself, focusing on memory scale and communication efficiency.

GPU Architecture and Process

Both platforms use the Hopper H100 GPU built on a 5 nm process. From a GPU perspective, compute capabilities are identical, including Tensor Core generation, supported precisions, and instruction set features.

Memory and Bandwidth

The H100 is equipped with 80 GB of HBM3 memory, delivering very high on-package bandwidth suitable for large models and high-throughput workloads. However, GPU memory remains separate from CPU memory, requiring explicit transfers.

GH200 combines H100’s HBM3 memory with Grace CPU memory into a coherent shared pool that can scale into the hundreds of gigabytes. This reduces memory pressure, enables larger working sets, and minimizes data movement overhead for memory-bound workloads.

Interconnect

H100 supports fourth-generation NVLink, providing up to 900 GB/s of GPU-to-GPU bandwidth for efficient multi-GPU scaling. PCIe Gen5 further improves system-level connectivity.

GH200 replaces traditional CPU-GPU interconnects with NVLink-C2C, delivering high-bandwidth, low-latency communication and memory coherence between the CPU and GPU. This is a key differentiator for tightly coupled workloads.

Compute Units

Because both platforms use the same H100 GPU, CUDA core counts, Tensor Core counts, and cache sizes are equivalent. Differences in performance arise from system architecture rather than GPU compute capability.

Power and System Considerations

H100 platforms focus on performance per watt at the GPU level, while GH200 optimizes system-level efficiency by reducing redundant data transfers and improving utilization. GH200 systems typically draw more power overall but can deliver better efficiency for certain workloads by shortening execution time.

Performance Benchmarks & Key Specifications

Although H100 and GH200 target different system designs, their performance characteristics are closely related. Both platforms are built around the same Hopper GPU, so differences in real-world performance largely come from memory architecture, interconnect design, and system-level efficiency, rather than raw GPU compute.

Compute Performance

At the GPU level, H100 and GH200 offer comparable compute capabilities because both use the Hopper H100 GPU. Performance gains over earlier generations are driven by several Hopper-specific improvements:

• Fourth-generation Tensor Cores optimized for AI workloads
  
  
• Transformer Engine with FP8 precision, enabling higher throughput with minimal accuracy impact
  
  
• Higher on-package memory bandwidth using HBM3
  
  
• Improved scheduling and execution efficiency across Streaming Multiprocessors

For workloads that are primarily GPU-bound-such as dense matrix multiplication or transformer layers that fit comfortably within GPU memory-both H100 and GH200 deliver similar per-GPU performance.

Memory Architecture and Bandwidth

Memory design is the most significant differentiator between the two platforms.

• H100 uses discrete CPU and GPU memory, connected through PCIe or NVLink at the system level. While bandwidth is high, data movement between CPU and GPU still requires explicit transfers.
  
  
• GH200 provides direct, coherent access between CPU and GPU memory, creating a large shared memory pool. This dramatically reduces data movement overhead and simplifies memory management.

For workloads with large memory footprints, frequent CPU-GPU synchronization, or complex data pipelines, GH200 can significantly reduce latency and improve effective throughput.

Interconnect and Scaling

Interconnect design plays a critical role at scale.

• H100 supports NVLink for high-bandwidth GPU-to-GPU communication, making it well suited for multi-GPU training and distributed inference.
  
  
• GH200 extends high-bandwidth interconnects to CPU-GPU communication using NVLink-C2C, enabling tighter coupling between compute and memory-heavy operations.

As systems scale across multiple GPUs or nodes, these architectural differences become more pronounced. In communication-heavy workloads, GH200 can reduce synchronization overhead that would otherwise limit performance.

Training Performance

For deep learning training workloads that are primarily GPU-bound, H100 and GH200 achieve similar per-GPU performance. Improvements over previous generations come from FP8 precision, enhanced Tensor Cores, and higher memory bandwidth.

However, when training involves large datasets, extensive CPU-side preprocessing, or memory pressure, GH200 can deliver higher effective training throughput by minimizing CPU-GPU bottlenecks and reducing idle time.

Inference Performance

H100 is optimized for high-throughput, low-latency inference, making it well suited for real-time applications such as conversational AI and code generation. Its Transformer Engine and memory bandwidth enable high token generation rates for large language models.

GH200 shows advantages in inference scenarios where model size, context length, or preprocessing requirements exceed typical GPU memory limits. By reducing data movement and enabling unified memory access, GH200 can improve tail latency and sustain throughput under heavy load.

High-Performance Computing (HPC) Workloads

For scientific and HPC workloads, H100 delivers strong FP64 and Tensor Core performance, supporting simulations, numerical modeling, and scientific computing.

GH200 extends these capabilities by enabling tighter coupling between CPU-based control logic and GPU-accelerated computation. This is particularly beneficial for memory-bound simulations, graph-based workloads, and applications where frequent CPU-GPU coordination would otherwise limit scalability.

Key Use Cases

When H100 Is the Better Fit

H100 is well suited for:

• Large language model training and inference
  
  
• High-throughput batch inference
  
  
• Latency-sensitive real-time applications
  
  
• Standard GPU-based AI infrastructure

For most production AI workloads today, H100 offers the best balance of performance, flexibility, and operational simplicity.

When GH200 Makes Sense

GH200 is more appropriate for:

• Memory-bound workloads that exceed typical GPU memory limits
  
  
• Large models with heavy CPU preprocessing or coordination
  
  
• Scientific simulations and HPC workloads with tight CPU-GPU coupling
  
  
• Systems where data movement, not compute, is the primary bottleneck

GH200 enables architectures that are difficult or inefficient to build with discrete CPUs and GPUs.

Tips for Choosing the Right GPU

• Start with H100 unless memory or CPU-GPU communication is a known constraint
  
  
• Consider GH200 only when unified memory or tighter system integration provides measurable benefits
  
  
• Benchmark workloads end-to-end rather than relying on peak FLOPS
  
  
• Factor in total system cost, including power, cooling, and operational complexity
  
  
• Avoid over-optimizing for future scale unless it is clearly required

Conclusion

The choice between H100 and GH200 depends primarily on workload profile rather than headline specifications. The H100 is a well-balanced accelerator that performs reliably across training, fine-tuning, and inference, making it a sensible default for most AI workloads, including large language models. It offers strong compute density and predictable behavior across a wide range of scenarios.

The GH200 is optimized for a narrower set of problems. It targets large, memory-bound workloads where CPU–GPU coordination and memory bandwidth are limiting factors. For models or pipelines that require tight coupling between large memory pools and sustained throughput, GH200 can reduce system-level bottlenecks that are harder to address with discrete accelerators alone.

In practice, hardware selection is rarely static. As models evolve, workloads shift between training, fine-tuning, and inference, and memory requirements change over time. For teams deploying their own models on custom hardware, Clarifai’s compute orchestration makes it possible to run the same models across different GPU types, including H100 and GH200, without redesigning infrastructure for each setup. This allows teams to evaluate, mix, and transition between accelerators as workload characteristics change, while keeping deployment and operations consistent.

If you need access to these custom GPUs for your own workloads, you can reach out to the team here. You can also join our Discord community to connect with the team and get guidance on optimizing and deploying your AI infrastructure.