H200 vs A30
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.
A30
The NVIDIA A30 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
H200 vs A30: Which Should You Choose?
The H200 offers 141 GB of VRAM — 6× the 24 GB on the A30 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H200 delivers 267.6 TFLOPS versus 10.32 TFLOPS on the A30 — 26× faster for mixed-precision training and inference. Memory bandwidth favors the A30 at 0.93 TB/s compared to 0.00 TB/s on the H200, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the H200 is built on Hopper while the A30 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the A30 starts from $0.35/hr versus $2.45/hr for the H200 — 600% more expensive — reflecting the performance premium. The H200 is available across 7 cloud providers on Shadeform compared to 1 for the A30, 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 are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
A30 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A30 when:
- ✓24 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
See how the H200 & A30 compare
Compare detailed hardware specifications and average pricing for the H200 and A30.
Compare Hardware Specifications
| H200 | A30 | |
|---|---|---|
| GPU Type | H200 | A30 |
| VRAM per GPU | 141 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Ampere |
| Interconnect | SXM5 | PCIe Gen4 |
| Memory Bandwidth | 4.8 TB/s | 933 GB/s |
| FP16 TFLOPS | 267.6 TFLOPS (4:1) | 10.32 TFLOPS (1:1) |
| CUDA Cores | 16896 | 3584 |
| Tensor Cores | 528 (4th Gen) | 224 (3rd Gen) |
| Base Clock | 1500 MHz | 930 MHz |
| Boost Clock | 1980 MHz | 1440 MHz |
| TDP | 350-700W | 165W |
| Process Node | TSMC 4N | TSMC 7nm |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32, FP64 | INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| H200 | A30 | |
|---|---|---|
| 1 GPU | $3.33 /hr | $0.35 /hr |
| 2 GPUs | $14.79 /hr | $0.70 /hr |
| 4 GPUs | $7.60 /hr | $1.40 /hr |
| 8 GPUs | $23.48 /hr | $2.80 /hr |
Frequently Asked Questions: H200 vs A30
The main differences are VRAM (141 GB vs 24 GB), FP16 throughput (267.6 vs 10.32 TFLOPS), architecture (Hopper vs Ampere). The H200 uses the Hopper architecture while the A30 is based on Ampere, giving each GPU different generational capabilities.
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 A30 is available from $0.35/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 24 GB on the A30. 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 H200 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 A30, 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 1 for the A30. 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 A30. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore H200 & A30 Instances
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