Specifications Compared
| Spec | A100 | GH200 |
|---|---|---|
| TDP | 400W | 900W |
| VRAM | 40-80 GB | 96 GB |
| CUDA Cores | 6,912 | 16,896 |
| Memory Type | HBM2e | HBM3 |
| Architecture | Ampere | Hopper |
| Form Factors | SXM4, PCIe | SXM |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink-C2C, PCIe 5.0 |
| Tensor Cores | 432 | 528 |
| FP16 Performance | 312 TFLOPS | 1,979 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 67 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 34 TFLOPS |
| INT8 Performance | 624 TOPS | 3,958 TOPS |
| Memory Bandwidth | 2,039 GB/s | 4,000 GB/s |
Performance Analysis
The GH200's FP16 performance of 1979 TFLOPS vastly exceeds A100's 312 TFLOPS: this accelerates deep learning training by enabling more iterations per hour. For inference, GH200's FP8 at 3958 TFLOPS supports ultra-efficient low-precision deployments, a feature unavailable on A100. FP32 compute on GH200 reaches 67 TFLOPS compared to 19.5 TFLOPS: scientific computing benefits from faster simulations.
Memory specifications favor GH200 decisively: 96 GB HBM3 VRAM and 4000 GB/s bandwidth handle larger models and batch sizes than A100's 40 GB HBM2e at 2039 GB/s. Larger batches reduce per-sample overhead in training, while high bandwidth minimizes data transfer bottlenecks during inference. GH200's 900W TDP demands more power than A100's 400W, impacting dense deployments.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 40GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 397GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 126GB RAM 1114GB Storage | Czechia | $1.00/GPU/hr $2.00/hr total (2×) | Available | ||
 Denvr | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) | |||
Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
GH200 Grace Hopper
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vultr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 960GB Storage | Atlanta | $1.99/GPU/hr | Available | ||
 Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | Virginia | $2.29/GPU/hr | Available | ||
Denvr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 7600GB Storage | Virginia | $3.87/GPU/hr | |||
 CoreWeave | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 7680GB Storage | United States | $6.50/GPU/hr |
When to Choose the A100 SXM4 40GB
Cost-sensitive projects select A100 SXM4 40GB: pricing from $1.00 per hour averages $2.63, undercutting GH200's $1.99 minimum and $3.33 average. Lower 400W TDP fits power-constrained clusters better than GH200's 900W. Mature Ampere ecosystem supports established workflows with 312 TFLOPS FP16 for mid-scale AI tasks.
When to Choose the GH200 Grace Hopper
Cutting-edge AI demands GH200 Grace Hopper: 96 GB VRAM accommodates massive LLMs where A100's 40 GB limits scale. Superior 1979 TFLOPS FP16 and 4000 GB/s bandwidth enable rapid training and large-batch inference. NVLink-C2C interconnect outperforms A100's NVLink for multi-GPU setups.
Use Cases
GH200's 1979 TFLOPS FP16 and 96 GB VRAM support massive models and large batches far beyond A100's 312 TFLOPS and 40 GB.
GH200's FP8 at 3958 TFLOPS and 4000 GB/s bandwidth enable high-throughput serving; A100 lacks FP8 and trails in memory capacity.
A100 suffices for smaller models at lower $2.63 hourly cost; GH200 excels for parameter-heavy fine-tuning with 67 TFLOPS FP32.
A100's 312 TFLOPS FP16 handles image generation efficiently at $1.00 per hour starting price; GH200 overkill for typical resolutions.
GH200's 67 TFLOPS FP32 outperforms A100's 19.5 TFLOPS for simulations; 4000 GB/s bandwidth aids complex datasets.
Frequently Asked Questions
What is the VRAM difference between A100 SXM4 40GB and GH200?▾
A100 SXM4 40GB offers 40 GB HBM2e VRAM. GH200 provides 96 GB HBM3 VRAM. This enables GH200 to manage larger models without swapping.
How do cloud prices compare for these GPUs?▾
A100 SXM4 40GB starts from $1.00 per hour, averaging $2.63 across five offers. GH200 starts at $1.99 per hour, averaging $3.33 across five offers.
Which has higher FP16 performance?▾
GH200 achieves 1979 TFLOPS FP16. A100 reaches 312 TFLOPS FP16. GH200 suits accelerated training workloads.
What are the memory bandwidth specs?▾
A100 delivers 2039 GB/s bandwidth. GH200 provides 4000 GB/s. Higher bandwidth on GH200 supports bigger batch sizes.
Does GH200 support FP8?▾
GH200 offers 3958 TFLOPS FP8 for efficient inference. A100 lacks FP8 capability. This boosts GH200 in deployment scenarios.
Which is cheaper to rent, the A100 or the GH200?▾
Cloud rental prices for both the A100 and GH200 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the A100 have compared to the GH200?▾
The A100 has 40 to 80 GB of HBM2e memory. The GH200 has 96 GB of HBM3 memory.
Can I find A100 and GH200 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the A100 and the GH200?▾
The A100 uses the Ampere architecture (2020) while the GH200 uses Hopper (2023). The GH200 delivers 6.3x the FP16 throughput and 2.0x the memory bandwidth of the A100.