The H100 PCIe is a high-performance datacenter GPU. Featuring 80GB of ultra-fast memory, it is engineered for the most demanding AI model training, large language models (LLMs), and complex scientific computing.
Recommended Scenarios
Deep Learning
Model Inference
Video Encoding
Architecture
Hopper
VRAM Capacity
80GB
Bandwidth
2.0 TB/s
CUDA Cores
14592
FP16 Perf.
1513 TFLOPS
Power (TDP)
350W
What Users Say
Real experiences from ML engineers and researchers
Training 70B parameter LLMReddit
"We switched from A100s to H100s for training our 70B parameter model. The speedup was honestly shocking — about 2.3x faster on identical workloads. The Transformer Engine with FP8 is the real deal. Yeah, it's expensive at $2-3/hr, but we cut our training time from 3 weeks to 10 days. Worth every penny for time-sensitive projects."
LLM inference at scaleTwitter
"H100s are incredible but getting them is a nightmare. Most providers have waitlists weeks long. We ended up paying premium on CoreWeave just to get immediate access. Once you have them though? Chef's kiss. We saw 3.2x speedup over A100 for inference with TensorRT-LLM."
Multi-node distributed trainingHacker News
"The NVLink on H100 is what makes it special. We run 8xH100 nodes and the GPU-to-GPU bandwidth is just absurd. No more bottlenecks during distributed training. Just be careful — some cloud providers only offer PCIe versions which lose that advantage. Always check if it's SXM5."
Startup fine-tuning workloadsReddit
"Look, H100s are overkill for most people. I trained a 7B model on them and it was done in 6 hours. Could've used 4090s for fraction of the cost. But if you're doing serious foundation model work? There's nothing else. The HBM3 bandwidth is noticeable on memory-bound workloads."
Academic research, long training runsDiscord
"Had stability issues with H100s on one provider (not naming names but rhymes with 'crusoe'). Kept getting CUDA errors after 8+ hours of training. Switched to Lambda and it's been rock solid. Moral of the story: the GPU is great but provider infrastructure matters a LOT."