GH200 vs H100
Explore a head to head comparison of specifications, performance, and pricing.
GH200
The NVIDIA GH200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.
H100
The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.
GH200 vs H100: Which Should You Choose?
The GH200 offers 96 GB of VRAM — 1.2× the 80 GB on the H100 — making it better suited for large model workloads that require holding more parameters in GPU memory. Memory bandwidth favors the GH200 at 0.00 TB/s compared to 0.00 TB/s on the H100, which directly impacts inference latency for memory-bandwidth-bound models. On Shadeform, the GH200 starts from $1.49/hr versus $1.66/hr for the H100 — 11% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 2 for the GH200, giving more options for region and pricing flexibility.
GH200 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose GH200 when:
- ✓You need 96 GB+ VRAM for large models or long context windows
- ✓Cost efficiency is your primary concern
- ✓Your preferred provider already has availability
H100 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H100 when:
- ✓80 GB VRAM is sufficient for your workload
- ✓Maximum performance justifies the higher cost
- ✓You need flexibility across multiple cloud providers or regions
See how the GH200 & H100 compare
Compare detailed hardware specifications and average pricing for the GH200 and H100.
Compare Hardware Specifications
| GH200 | H100 | |
|---|---|---|
| GPU Type | GH200 | H100 |
| VRAM per GPU | 96 GB | 80 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Hopper |
| Interconnect | NVLink-C2C | PCIe Gen5 or SXM5 |
| Memory Bandwidth | 4 TB/s or 4.9 TB/s | 3.35 TB/s |
| FP16 TFLOPS | 267.6 TFLOPS (4:1) | 267.6 TFLOPS (4:1) |
| CUDA Cores | 16896 | 16896 |
| Tensor Cores | 528 (4th Gen) | 528 (4th Gen) |
| Base Clock | 1500 MHz | 1365 MHz |
| Boost Clock | 1980 MHz | 1785 MHz |
| TDP | 900W-1000W | 350-700W |
| Process Node | TSMC 4N | TSMC 4N |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| GH200 | H100 | |
|---|---|---|
| 1 GPU | $2.86 /hr | $2.85 /hr |
| 2 GPUs | N/A | $5.19 /hr |
| 4 GPUs | N/A | $9.79 /hr |
| 8 GPUs | N/A | $19.35 /hr |
Frequently Asked Questions: GH200 vs H100
The main differences are VRAM (96 GB vs 80 GB).
The GH200 is generally better for large language model training due to its higher throughput and 96 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the GH200 is available from $1.49/hr. The H100 starts from $1.66/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The GH200 has more VRAM at 96 GB, compared to 80 GB on the H100. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the GH200 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the H100, paying the premium may be justified by faster job completion and lower total cost.
The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 2 for the GH200. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either GH200 or H100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore GH200 & H100 Instances
Browse available instances with GH200 and H100 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
Explore more GPU comparisons
Select any two GPUs to compare their specifications and explore pricing across providers.