GH200 vs RTX 6000 Ada
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.
RTX 6000 Ada
The NVIDIA RTX 6000 Ada delivers high-performance computing capabilities for AI, machine learning, and data science applications.
GH200 vs RTX 6000 Ada: Which Should You Choose?
The GH200 offers 96 GB of VRAM — 2× the 48 GB on the RTX 6000 Ada — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the GH200 delivers 267.6 TFLOPS versus 91.06 TFLOPS on the RTX 6000 Ada — 3× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 6000 Ada at 0.96 TB/s compared to 0.00 TB/s on the GH200, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the GH200 is built on Hopper while the RTX 6000 Ada uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 6000 Ada starts from $0.97/hr versus $1.49/hr for the GH200 — 54% more expensive — reflecting the performance premium. The RTX 6000 Ada is available across 4 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
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓Your preferred provider already has availability
RTX 6000 Ada — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 6000 Ada when:
- ✓48 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓You need flexibility across multiple cloud providers or regions
See how the GH200 & RTX 6000 Ada compare
Compare detailed hardware specifications and average pricing for the GH200 and RTX 6000 Ada.
Compare Hardware Specifications
| GH200 | RTX 6000 Ada | |
|---|---|---|
| GPU Type | GH200 | RTX 6000 Ada |
| VRAM per GPU | 96 GB | 48 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Ada Lovelace |
| Interconnect | NVLink-C2C | PCIe Gen4 |
| Memory Bandwidth | 4 TB/s or 4.9 TB/s | 960 GB/s |
| FP16 TFLOPS | 267.6 TFLOPS (4:1) | 91.06 TFLOPS (1:1) |
| CUDA Cores | 16896 | 18176 |
| Tensor Cores | 528 (4th Gen) | 568 (4th Gen) |
| RT Cores | N/A | 142 (3rd Gen) |
| Base Clock | 1500 MHz | 915 MHz |
| Boost Clock | 1980 MHz | 2505 MHz |
| TDP | 900W-1000W | 300W |
| Process Node | TSMC 4N | TSMC 4N |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| GH200 | RTX 6000 Ada | |
|---|---|---|
| 1 GPU | $2.86 /hr | $1.20 /hr |
| 2 GPUs | N/A | $2.04 /hr |
| 4 GPUs | N/A | $3.88 /hr |
| 8 GPUs | N/A | $7.01 /hr |
Frequently Asked Questions: GH200 vs RTX 6000 Ada
The main differences are VRAM (96 GB vs 48 GB), FP16 throughput (267.6 vs 91.06 TFLOPS), architecture (Hopper vs Ada Lovelace). The GH200 uses the Hopper architecture while the RTX 6000 Ada is based on Ada Lovelace, giving each GPU different generational capabilities.
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 RTX 6000 Ada is available from $0.97/hr. The GH200 starts from $1.49/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 48 GB on the RTX 6000 Ada. 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 RTX 6000 Ada, paying the premium may be justified by faster job completion and lower total cost.
The RTX 6000 Ada is currently available across 4 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 RTX 6000 Ada. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore GH200 & RTX 6000 Ada Instances
Browse available instances with GH200 and RTX 6000 Ada 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.
