AI Insights
The NVIDIA H100 Tensor Core GPU is the current gold standard for large-scale AI training and inference. Featuring the Transformer Engine and 80GB of HBM3 memory, it offers up to 9x faster AI training over the previous generation A100. On CloudGPUTracker, we monitor H100 instances from global providers. Whether you need a single PCIe card for development or an 8x SXM cluster for massive LLM fine-tuning, our tracker helps you find the immediate availability across global providers.
The H100 is a high-performance Inferred GPU. Featuring 80GB HBM3 of ultra-fast memory, it is engineered for the most demanding AI model training, large language models (LLMs), and complex scientific computing.
Recommended Scenarios
LLM Training
Advanced AI Research
Deep Learning Fine-tuning
Architecture
Hopper
VRAM Capacity
80GB HBM3
Bandwidth
3350 GB/s
CUDA Cores
16896
FP16 Perf.
1979 TFLOPS
Power (TDP)
700W
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."