NVIDIA H100 GPU Servers
The flagship GPU for AI training. 3x faster than A100, with FP8 precision and 3.35 TB/s memory bandwidth.
80GB HBM3
HBM3 Memory
3.35 TB/s
Memory Bandwidth
3,958 TFLOPS
FP8 Performance
3x
Faster than A100
H100 Technical Specs
Hopper architecture built for AI/ML workloads
Compute
- CUDA Cores16,896
- Tensor Cores528
- FP32 Performance67 TFLOPS
- FP16 Tensor1,979 TFLOPS
- FP8 Tensor3,958 TFLOPS
Memory
- Memory Size80GB HBM3
- Memory Bandwidth3.35 TB/s
- Memory TypeHBM3
- ECCYes
Connectivity
- InterconnectNVLink 4.0 (900 GB/s)
- PCIeGen5 x16
- TDP700W
- Form FactorSXM5
H100 Pricing
Flexible pricing for every workload
Reserved (Monthly)
- ~17% savings vs on-demand
- Guaranteed availability
- Priority support
What Can You Build with H100?
Industry-leading performance for the most demanding AI workloads
LLM Training
Train large language models with 100B+ parameters. H100 delivers 3x faster training vs A100 for transformer architectures.
Foundation Model Fine-tuning
Fine-tune LLaMA, Mistral, Falcon, and other foundation models with full precision or LoRA/QLoRA.
Distributed Training
Scale across 8x H100 clusters with NVLink for near-linear scaling on large training jobs.
High-Throughput Inference
Deploy models for production inference with industry-leading throughput and low latency.
H100 vs Other GPUs
H100 vs A100
3x faster training, 30% better inference throughput, FP8 support
H100 vs H200
H200 has 76% more memory (141GB vs 80GB) for larger models
H100 vs L40S
H100 is 4x faster for training, L40S better for cost-effective inference
Explore other GPUs:
H100 Questions
What is the difference between H100 SXM and H100 PCIe?
H100 SXM offers higher memory bandwidth (3.35 TB/s vs 2 TB/s) and NVLink 4.0 support for multi-GPU scaling. SXM is preferred for training workloads, while PCIe is suitable for inference.
How does H100 pricing compare to A100?
H100 costs approximately 50% more per hour than A100, but delivers 3x better training performance. For training workloads, H100 typically offers better cost-efficiency despite the higher hourly rate.
Can I use H100 for inference workloads?
Yes. H100 excels at inference, especially with FP8 quantization delivering 4x higher throughput than FP16. However, for cost-sensitive inference, A100 or L40S may offer better value.
What ML frameworks support H100?
All major frameworks including PyTorch 2.0+, TensorFlow, JAX, and specialized libraries like DeepSpeed and Megatron-LM fully support H100 and its FP8 capabilities.
Do you offer multi-GPU H100 instances?
Yes. We offer 1x, 2x, 4x, and 8x H100 configurations with NVLink interconnect. For larger clusters, contact our team for custom deployments.
Start Training on H100 Today
Get instant access to H100 GPUs with pre-configured ML environments.