How does NVIDIA A30 benchmark against H100?
💡 Answer
Benchmarked performance on NVIDIA A30: 165 TFLOPS in FP16, 10.3 TFLOPS in FP32, 933 GB/s memory bandwidth, 24 GB VRAM.
For the workloads most engineers care about — model training transformer-family models, serving LLM low-latency inference, running diffusion and vision pipelines — those specs are enough to sustain batch sizes that keep tensor cores busy. Expect wall-clock gains versus previous-generation Ampere cards to range from 1.5x to 3x depending on workload shape.
Launch a NVIDIA A30 instance on Massed Compute at $0.25/hr, or try RunPod for alternative regions and availability.
More FAQs about NVIDIA A30
Massed Compute vs RunPod - GPU Provider Comparison (April 2026)
Head-to-head comparison of Massed Compute and RunPod. Compare GPU models, hourly pricing, billing granularity, spot instances, VRAM, infrastructure, developer tools, Kubernetes support, and compliance before choosing a provider. Data refreshed April 2026.
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Massed Compute
GPU cloud with direct engineer support
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RunPod
The cloud built for AI — deploy and scale GPU workloads from serverless inference to instant multi-node clusters on demand.
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|---|---|---|
| Overview | ||
| Trustpilot Rating | 0 | 3.7 |
| Headquarters | United States | United States |
| Provider Type | GPU-Focused | GPU-Focused |
| Best For | AI training inference VFX rendering generative AI fine-tuning HPC Stable Diffusion research | AI training inference fine-tuning Stable Diffusion batch processing rendering research LLM serving generative AI |
| GPU Hardware | ||
| GPU Models | A30 RTX A5000 RTX A6000 L40S A100 SXM H100 PCIe H100 SXM H100 NVL RTX PRO 6000 H200 NVL | B300 B200 H200 H100 SXM H100 PCIe H100 NVL MI300X A100 SXM A100 PCIe RTX 5090 RTX PRO 6000 L40S L40 RTX 6000 Ada RTX 5000 Ada RTX A6000 RTX A5000 RTX 4090 RTX 4080 SUPER RTX 4080 RTX 4070 Ti RTX 3090 Ti RTX 3090 RTX 3080 Ti RTX 3080 RTX 3070 A40 A30 A2 L4 |
| Max VRAM (GB) | 141 | 288 |
| Max GPUs/Instance | 8 | 8 |
| Interconnect | NVLink | NVLink |
| Pricing | ||
| Starting Price ($/hr) | $0.35/hr | $0.06/hr |
| Billing Granularity | Per-minute | Per-second |
| Spot/Preemptible | No | Yes |
| Reserved Discounts | N/A | 15-29% (1-month to 1-year plans) |
| Free Credits | None | $5-$500 bonus after first $10 spend |
| Egress Fees | None | None (Free) |
| Storage | Local NVMe included with instances | Container/Volume ($0.10/GB/mo), Idle Volume ($0.20/GB/mo), Network Storage ($0.07/GB/mo 1TB) |
| Infrastructure | ||
| Regions | United States (Tier III data centers) | 31 global regions |
| Uptime SLA | Tier III (99.98% design) | 99.99% |
| Developer Experience | ||
| Frameworks | PyTorch TensorFlow CUDA cuDNN ComfyUI pre-configured ML templates | PyTorch TensorFlow JAX ONNX CUDA |
| Docker Support | Yes | Yes |
| SSH Access | Yes | Yes |
| Jupyter Notebooks | No | Yes |
| API / CLI | Yes | Yes |
| Setup Time | Minutes | Instant |
| Kubernetes Support | No | No |
| Business Terms | ||
| Min Commitment | None | None |
| Compliance | SOC 2 Type II HIPAA | SOC 2 Type II |
Massed Compute
RunPod