Specifications Compared
| Spec | A30 | H200 |
|---|---|---|
| TDP | 165W | 700W |
| VRAM | 24 GB | 141 GB |
| CUDA Cores | 3,584 | 16,896 |
| Memory Type | HBM2 | HBM3e |
| Architecture | Ampere | Hopper |
| Form Factors | PCIe | SXM, NVL |
| Interconnect | NVLink | NVLink, PCIe 5.0, InfiniBand |
| Tensor Cores | 224 | 528 |
| FP16 Performance | 10.3 TFLOPS | 1,979 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 67 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | 34 TFLOPS |
| INT8 Performance | 165 TOPS | 3,958 TOPS |
| Memory Bandwidth | 933 GB/s | 4,800 GB/s |
Performance Analysis
The H200's FP16 performance reaches 1979 TFLOPS compared to the A30's 10.3 TFLOPS, enabling dramatically faster neural network training where half-precision compute dominates. FP32 throughput of 67 TFLOPS on the H200 versus 10.3 TFLOPS on the A30 accelerates simulations and graphics workloads reliant on single-precision arithmetic.
Inference benefits immensely from the H200's FP8 support at 3958 TFLOPS, allowing quantized model deployment at scales the A30 cannot match without such capability. Memory capacity disparity defines real-world limits: 141 GB HBM3e on the H200 accommodates entire large language models, while 24 GB HBM2 on the A30 restricts users to smaller variants or reduced batch sizes.
Bandwidth of 4800 GB/s on the H200 versus 933 GB/s on the A30 permits larger batch processing during training, minimizing data bottlenecks and shortening epochs. These metrics translate to H200 completing complex workloads in fractions of the A30's time, though at higher 700W TDP versus 165W.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H200 NVL
| 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 | ||
 Nebius | NVIDIA H200 SXM 141GB VRAM | 141GB | 16 vCPU 200GB RAM | 🌍Europe | $2.45/GPU/hr | |||
 CoreWeave | 8×NVIDIA H200 SXM 141GB VRAM | 141GB | 128 vCPU 0GB RAM 61440GB Storage | United States | $2.58/GPU/hr $20.64/hr total (8×) | |||
 Ori | NVIDIA H200 SXM 141GB VRAM | 141GB | 24 vCPU 240GB RAM 3000GB Storage | London | $3.50/GPU/hr | Available |
When to Choose the A30
The A30 fits low-power edge deployments with its 165W TDP, contrasting the H200's 700W requirement. PCIe form factor simplifies integration into standard servers without specialized cooling or power infrastructure.
Cost-sensitive legacy applications benefit from the A30's balanced 10.3 TFLOPS FP16 and FP32 performance, especially where no cloud offers exist and on-premise hardware predominates.
When to Choose the H200 NVL
The H200 targets large-scale AI training and inference, leveraging 141 GB HBM3e VRAM and 4800 GB/s bandwidth to process massive models undreamt by the A30's 24 GB HBM2.
Multi-GPU clusters thrive on H200's NVLink, PCIe 5.0, and InfiniBand, with cloud pricing from $0.50 per hour enabling scalable access across five providers.
Use Cases
H200's 141 GB HBM3e VRAM and 1979 TFLOPS FP16 handle massive models and large batches infeasible on A30's 24 GB HBM2.
FP8 performance at 3958 TFLOPS and 4800 GB/s bandwidth on H200 enable efficient high-throughput serving, surpassing A30's capabilities.
H200's 67 TFLOPS FP32 and superior memory support rapid iteration on large models, while A30 struggles with capacity.
A30's 10.3 TFLOPS FP16 suffices for standard image generation; H200 accelerates complex variants with more VRAM.
H200's 67 TFLOPS FP32 outperforms A30's 10.3 TFLOPS for precision simulations, aided by higher bandwidth.
Frequently Asked Questions
What is the VRAM difference between A30 and H200?▾
A30 provides 24 GB HBM2 VRAM. H200 offers 141 GB HBM3e VRAM. This enables H200 to load significantly larger models without offloading.
How do FP16 performances compare?▾
A30 delivers 10.3 TFLOPS FP16. H200 achieves 1979 TFLOPS FP16. The gap accelerates deep learning training substantially on H200.
What are the power requirements?▾
A30 has 165W TDP in PCIe form. H200 requires 700W TDP in SXM or NVL. Lower TDP suits A30 for constrained environments.
Is cloud pricing available for these GPUs?▾
A30 has no live cloud offers. H200 NVL starts at $0.50 per hour, averaging $2.60 across five providers.
What interconnects do they support?▾
Both use NVLink. H200 adds PCIe 5.0 and InfiniBand for advanced clustering; A30 relies on PCIe form factor.
Which has higher memory bandwidth?▾
H200 provides 4800 GB/s. A30 offers 933 GB/s. Higher bandwidth on H200 supports larger batches and faster data movement.
Which is cheaper to rent, the A30 or the H200?▾
Cloud rental prices for both the A30 and H200 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 A30 have compared to the H200?▾
The A30 has 24 GB of HBM2 memory. The H200 has 141 GB of HBM3e memory.
Can I find A30 and H200 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 A30 and the H200?▾
The A30 uses the Ampere architecture (2021) while the H200 uses Hopper (2024). The H200 delivers 192.1x the FP16 throughput and 5.1x the memory bandwidth of the A30.