Rent an NVIDIA H100 PCIE GPU with 80 GB VRAM
Rent H100 PCIE GPUs by the hour on Cloudj: 80 GB VRAM on NVIDIA Hopper, about 41 TFLOPS FP32 and 1760 GB/s memory bandwidth for training and LLM inference. It is the entry point to Hopper for AI teams that want 80 GB on a single card at a 350 W power limit. Pick a live configuration, start from the Ubuntu 22.04 + CUDA or PyTorch + JupyterLab template and connect over SSH with your key.
H100 PCIE specifications
VRAM per GPU
80 GB
Architecture
NVIDIA Hopper
Compute capability
9
FP32 compute
≈ 41 TFLOPS
Memory bandwidth
≈ 1760 GB/s
Power limit
350 W
Values reported by the infrastructure provider for machines currently listed; real performance varies by machine.
Live H100 PCIE configurations and prices
Out of stock right now
Browse available GPUsWhat you can run on the H100 PCIE cloud GPU
Fine-tuning 13B–34B models
80 GB fits LoRA on 30B-class models in BF16 and QLoRA on 70B-class models with Unsloth.
Serving quantized 70B models
Host a 4-bit 70B model with vLLM or SGLang on one GPU, so you avoid multi-GPU sharding during prototyping.
Mixed-precision training
Train transformers and diffusion models from scratch or continue pre-training on Hopper with compute capability 9.0.
Video and image model research
Run large diffusion and video generation models in ComfyUI that need more memory than any consumer card.
Is the H100 PCIE the right GPU for your workload?
Choose the H100 PCIE when your workloads require 80 GB of VRAM, datacenter-grade memory bandwidth, and Hopper compute capability 9.0 features. For lighter inference or smaller models, a workstation or consumer GPU with less VRAM is more economical, while extreme throughput requirements may warrant an SXM-based datacenter variant.
Guides for H100 PCIE
H100 PCIE cloud GPU: frequently asked questions
Rent the H100 PCIE cloud GPU by the hour
Pick a live configuration, choose a template and connect over SSH with your key. Billing stops when you destroy the instance.