GPU NVIDIA

GH200

Architecture · 96GB · PCIe

VRAM
96GB
FP16
-
TDP
-
Hardware Price
-
Cloud from
$1.49/hr
1 providers
Cheapest at Lambda Labs →

Quick Insights

Performance/Dollar
N/A
FP16 performance per $1000
VRAM/Dollar
N/A
VRAM per $1000
vs null Average
N/A
FP16 TFLOPS comparison
Cloud Availability
1 providers
from $1.49/hr

Specifications

VRAM 96GB
Memory Bandwidth -
FP16 TFLOPS -
Tensor TFLOPS -
FP32 TFLOPS -
TDP -
Form Factor -
Architecture -
NVLink No
Release Date -

Cloud GPU Pricing

Rent GH200 from 1 cloud providers. Prices shown per GPU per hour.

Provider Type Instance GPUs On-Demand Per GPU Spot Availability
Lambda Labs gpu-cloud lambda-gh200 1x $1.49/hr $1.49/hr Cheapest - -

GH200 vs Alternatives

Compare GH200 with similar GPUs from other brands.

GPU VRAM FP16 TFLOPS Bandwidth Hardware Price Cloud Price
GH200 Current 96GB - - - - -
MI300 AMD 128GB (+33%) 490.3 5.3 TB/s $15k - Compare
AMD Instinct MI300A AMD 128GB (+33%) 980.0 5.3 TB/s - - Compare
AMD Instinct MI250X AMD 128GB (+33%) 383.0 3.3 TB/s - - Compare
AMD Instinct MI250 AMD 128GB (+33%) 362.0 3.3 TB/s - - Compare
AMD Instinct MI210 AMD 64GB (-33%) 181.0 1.6 TB/s - - Compare

Best Use Cases

No specific use case recommendations for GH200 yet.

Browse All Use Cases →

Compare GH200

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Frequently Asked Questions about GH200

Pricing for GH200 varies. Check our cloud pricing section for rental options starting at $1.49/hr.

Yes, the GH200 with 96GB VRAM is suitable for many AI/ML workloads. For large language models, you may need multiple GPUs or consider higher-VRAM options like A100 or H100.

Consider buying for long-term, heavy usage (>4 hrs/day). Rent from cloud providers for short-term projects, experimentation, or when you need to scale quickly.

With 96GB VRAM and - FP16 TFLOPS, the GH200 can run: Large language models (7B-13B), Stable Diffusion XL, video AI, and professional 3D rendering.

The GH200 offers 96GB VRAM and - FP16 performance at its price point. Compare with similar GPUs using our comparison tool above. Key factors: VRAM for model size, TFLOPS for speed, and price for budget.