H200 vs GH200
Explore a head to head comparison of specifications, performance, and pricing.
H200
The NVIDIA H200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.
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.
H200 vs GH200: Which Should You Choose?
The H200 offers 141 GB of VRAM — 1.5× the 96 GB on the GH200 — making it better suited for large model workloads that require holding more parameters in GPU memory. Memory bandwidth favors the H200 at 0.00 TB/s compared to 0.00 TB/s on the GH200, which directly impacts inference latency for memory-bandwidth-bound models. On Shadeform, the GH200 starts from $1.49/hr versus $2.45/hr for the H200 — 64% more expensive — reflecting the performance premium. The H200 is available across 7 cloud providers on Shadeform compared to 2 for the GH200, giving more options for region and pricing flexibility.
H200 — 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 H200 when:
- ✓You need 141 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You need flexibility across multiple cloud providers or regions
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:
- ✓96 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your preferred provider already has availability
See how the H200 & GH200 compare
Compare detailed hardware specifications and average pricing for the H200 and GH200.
Compare Hardware Specifications
| H200 | GH200 | |
|---|---|---|
| GPU Type | H200 | GH200 |
| VRAM per GPU | 141 GB | 96 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Hopper |
| Interconnect | SXM5 | NVLink-C2C |
| Memory Bandwidth | 4.8 TB/s | 4 TB/s or 4.9 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 | 1500 MHz |
| Boost Clock | 1980 MHz | 1980 MHz |
| TDP | 350-700W | 900W-1000W |
| 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
| H200 | GH200 | |
|---|---|---|
| 1 GPU | $3.33 /hr | $2.86 /hr |
| 2 GPUs | $14.79 /hr | N/A |
| 4 GPUs | $7.60 /hr | N/A |
| 8 GPUs | $23.48 /hr | N/A |
Frequently Asked Questions: H200 vs GH200
The main differences are VRAM (141 GB vs 96 GB).
The H200 is generally better for large language model training due to its higher throughput and 141 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 H200 starts from $2.45/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The H200 has more VRAM at 141 GB, compared to 96 GB on the GH200. 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 H200, paying the premium may be justified by faster job completion and lower total cost.
The H200 is currently available across 7 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 H200 or GH200. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore H200 & GH200 Instances
Browse available instances with H200 and GH200 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
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