The NVIDIA H100 is the most widely deployed data-center GPU for production AI, 80 GB of HBM3 memory at 3.35 TB/s, 4th-gen Tensor Cores, and the first Transformer Engine purpose-built for large language models. Available on packet.ai from $2.50/GPU-hour.
Hopper introduced the Transformer Engine, hardware specifically designed to accelerate the attention and feed-forward layers that dominate LLM compute.
FP8 Tensor Cores deliver 3× the throughput of A100 on transformer workloads. The current frontier training standard.
HBM3 delivers 68% more bandwidth than A100. Critical for large batch sizes and long context lengths.
900 GB/s NVLink for tight multi-GPU coupling. Essential for tensor-parallel training across 8+ GPUs.
Hardware-accelerated FP8 mixed precision with dynamic scaling. Native support in PyTorch and JAX.
80 GB HBM3 and the Transformer Engine handle 7B–70B models in production with sub-100ms latency.
The most common GPU for training frontier models, mature tooling, widespread framework support.
Run PPO, DPO, and GRPO workflows on bursty hourly capacity.
H100 is launching soon on packet.ai. Join the waitlist to be notified.
Join waitlist →The H100 is NVIDIA’s flagship Hopper GPU, 80 GB HBM3 at 3.35 TB/s, the first Transformer Engine, and the most widely deployed GPU for LLM training.
H100 starts at $2.50/GPU-hour dynamic. See pricing when available.
Same compute die. H200 upgrades to HBM3e: 141 GB vs 80 GB and 4.8 TB/s vs 3.35 TB/s.
H100 is roughly 3× faster for LLM workloads: 990 vs 312 TFLOPS FP16, plus Transformer Engine.
Yes, up to 7 isolated MIG instances per H100.
On Dynamic, SSH-ready in under 5 minutes.
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