What is the FLOPS performance of the NVIDIA H100 GPU?
Understanding the FLOPS Performance of the NVIDIA H100 GPU
The NVIDIA H100 GPU, built on the Hopper architecture, represents NVIDIA's latest advancement in GPU technology, optimized for high-performance computing (HPC), AI training, and deep learning workloads. Its floating-point performance (measured in FLOPS—floating point operations per second) significantly surpasses previous GPU generations.
Below is a detailed breakdown of the NVIDIA H100 GPU's FLOPS performance across various precision formats.
NVIDIA H100 GPU FLOPS Specifications
The NVIDIA H100 GPU's theoretical peak FLOPS rates typically depend on the precision format being used. NVIDIA has optimized the H100 GPU for diverse workloads by supporting multiple precision formats, including FP64 (double precision), FP32 (single precision), FP16 (half precision), and INT8 precision.
1. FP64 (Double Precision) Performance
- FP64 Tensor Core: Up to approximately 67 TFLOPS (Tensor Core accelerated)
- FP64 Standard (non-Tensor Core): Approximately 34 TFLOPS
2. FP32 (Single Precision) Performance
- FP32 Standard (non-Tensor Core): Approximately 67 TFLOPS
- FP32 Tensor Core Accelerated: Up to approximately 1,000 TFLOPS (1 PFLOPS) (when using Transformer Engine and sparsity features)
3. FP16 (Half Precision) Performance
- FP16 Tensor Core: Up to 2,000 TFLOPS (2 PFLOPS) with Tensor Core acceleration and sparsity enabled.
4. INT8 Precision Performance (AI Inference)
- INT8 Tensor Core: Up to 4,000 TOPS (4 PetaOPS) with Tensor Core acceleration and sparsity enabled.
Summary Table of NVIDIA H100 GPU FLOPS Performance
| Precision Format | Peak Performance (Tensor Core Accelerated, Sparsity Enabled) | Typical Performance (No Sparsity) |
|---|---|---|
| FP64 | ~67 TFLOPS (Tensor Core) | ~34 TFLOPS (Standard) |
| FP32 | ~1,000 TFLOPS (1 PFLOPS) | ~67 TFLOPS (Standard) |
| FP16 | ~2,000 TFLOPS (2 PFLOPS) | ~1,000 TFLOPS (1 PFLOPS) |
| INT8 | ~4,000 TOPS (4 PetaOPS) | ~2,000 TOPS (2 PetaOPS) |
Factors Influencing NVIDIA H100 GPU FLOPS Performance
The theoretical peak performance of the NVIDIA H100 GPU can vary depending on several factors, including:
- Precision and data types used: Lower precision (FP16, INT8) typically results in higher throughput compared to higher-precision formats (FP64, FP32).
- Tensor Core utilization: Maximum performance is achieved through effective use of Tensor Cores.
- Use of Hopper's Transformer Engine and sparsity acceleration: Leveraging these features significantly enhances AI model performance and efficiency.
Practical Use Cases for NVIDIA H100 GPU
Given its extraordinary performance, the NVIDIA H100 GPU is ideal for:
- High-performance computing (HPC) applications (scientific simulations, computational fluid dynamics, molecular dynamics, etc.)
- Large-scale AI and deep learning training (natural language processing, computer vision, large transformer-based models)
- Real-time inference workloads requiring extremely fast, low-latency inference at scale (e.g., recommendation systems, conversational AI, autonomous vehicles)
Conclusion: NVIDIA H100 GPU Performance
The NVIDIA H100 GPU offers unprecedented floating-point performance, particularly when using Tensor Cores and advanced features such as sparsity acceleration and Transformer Engine. With peak performance reaching multiple petaflops for AI workloads, the H100 GPU significantly advances computing capabilities for both research and enterprise environments.