When it comes to choosing a GPU for AI, HPC, or data analytics, the NVIDIA H100 series has you covered. But with four different models—DGX, NVL, PCIe, and SXM—how do you know which one is right for you? Here’s a quick breakdown to help you decide.
NVIDIA H100 DGX
DGX H100 gives you top-tier performance for AI training and large-scale HPC tasks. It has eight H100 GPUs with 900 GB/s bandwidth and 640GB memory, making it a beast for handling massive datasets and complex models like GPT-3. It does come with a 10kW power requirement, so make sure your infrastructure can handle it.
NVIDIA H100 NVL
NVL is all about AI inference. With dual GPUs and 188GB memory, it delivers 12x faster performance compared to the older A100 model. If you’re focused on deploying large language models in real-time, especially in industries like healthcare or finance, this one’s a solid choice.
NVIDIA H100 PCIe
If you need flexibility, the H100 PCIe model is your go-to. It integrates easily with standard server setups using PCIe Gen 5.0 and comes in 80GB or 96GB variants. Its MIG technology allows you to partition the GPU for multiple tasks, making it perfect for scaling AI operations without overhauling your infrastructure.
NVIDIA H100 SXM
H100 SXM is for those who need extreme performance. With 60 TFLOPS FP64 and 1000 TFLOPS FP8 compute power, it’s built for AI training and heavy-duty HPC tasks like climate modeling or genomic research. Just note that it requires liquid cooling and specialized infrastructure, so make sure you’re ready for that commitment.
Which One to Pick?
DGX H100: Top performance for AI training and HPC.
H100 NVL: Fast AI inference and real-time deployment.
H100 PCIe: Flexible, scalable, and works with standard servers.
H100 SXM: Best for extreme AI and HPC, but requires specialized cooling.