H20
nvidia · hopper · 96 GB HBM3 · 500W TDP
VRAM
96 GB
BF16 TFLOPS
148
Bandwidth
4000 GB/s
From
$0.99/hr
Spec Sheet
Pricing by Provider
| Provider | On-Demand | Reserved | Spot | Badge |
|---|---|---|---|---|
| tensordock | $1.39/hr | - | $0.99/hr | Cheapest |
| vast_ai | $1.50/hr | - | $1.10/hr |
Compatible Models (278)
Single GPU (228 models)
Multi-GPU (50 models)
Training Capabilities
Estimated GPU count for full fine-tuning (AdamW, BF16) and QLoRA
| Model Size | Full Fine-Tune | QLoRA |
|---|---|---|
| 7B model | 2 GPUs | 1 GPU |
| 13B model | 3 GPUs | 1 GPU |
| 70B model | 14 GPUs | 1 GPU |
Energy Efficiency
Estimated tokens/second per Watt for popular models
Similar GPUs
Methodology Note
Performance estimates for the H20are based on InferenceBench's roofline performance model with CUDA kernel-level optimization including FlashAttention v2 and PagedAttention. Memory calculations account for model weights (96 GB HBM3 available), KV-cache allocation, and activation memory. Throughput predictions use the H20's rated 4000 GB/s memory bandwidth and 148 BF16 TFLOPS compute capacity as roofline ceilings, with empirical correction factors per GPU architecture (hopper). See our full methodology.
Frequently Asked Questions
How many AI models can run on H20?
The H20 can run 278 AI models from our database within a single node. Compatible models range across various parameter sizes depending on the quantization precision (BF16, FP8, INT4). Smaller models fit on a single GPU while larger models may require multi-GPU setups up to 8x H20.
What is the H20 inference throughput?
The H20 delivers 148 BF16 TFLOPS and 296 FP8 TFLOPS with 4000 GB/s memory bandwidth. Actual inference throughput (tokens/sec) depends on the model size, precision, and batch size. Use our calculator for model-specific throughput estimates.
How much does H20 cost per hour?
The H20 is available starting from $0.99/hour via tensordock. Prices vary by provider and pricing tier (on-demand, reserved, spot). Compare pricing across all providers in the table above.