A30 vs V100
Explore a head to head comparison of specifications, performance, and pricing.
A30
The NVIDIA A30 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
V100
The NVIDIA V100 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A30 vs V100: Which Should You Choose?
The A30 offers 24 GB of VRAM — 1.5× the 16 GB on the V100 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the V100 delivers 28.26 TFLOPS versus 10.32 TFLOPS on the A30 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the A30 at 0.93 TB/s compared to 0.90 TB/s on the V100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A30 is built on Ampere while the V100 uses Volta, reflecting different generational capabilities and optimizations. On Shadeform, the A30 starts from $0.35/hr versus $0.39/hr for the V100 — 11% more expensive — reflecting the performance premium. The V100 is available across 3 cloud providers on Shadeform compared to 1 for the A30, giving more options for region and pricing flexibility.
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:
- ✓You need 24 GB+ VRAM for large models or long context windows
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
V100 — Best Use Cases
- •Deep learning training
- •HPC and scientific computing
- •Legacy ML infrastructure
Choose V100 when:
- ✓16 GB VRAM is sufficient for your workload
- ✓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
See how the A30 & V100 compare
Compare detailed hardware specifications and average pricing for the A30 and V100.
Compare Hardware Specifications
| A30 | V100 | |
|---|---|---|
| GPU Type | A30 | V100 |
| VRAM per GPU | 24 GB | 16 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Volta |
| Interconnect | PCIe Gen4 | PCIe Gen3 |
| Memory Bandwidth | 933 GB/s | 900 GB/s |
| FP16 TFLOPS | 10.32 TFLOPS (1:1) | 28.26 TFLOPS (2:1) |
| CUDA Cores | 3584 | 5120 |
| Tensor Cores | 224 (3rd Gen) | 640 (1st Gen) |
| Base Clock | 930 MHz | 1230 MHz |
| Boost Clock | 1440 MHz | 1380 MHz |
| TDP | 165W | 250-300W |
| Process Node | TSMC 7nm | TSMC 12nm |
| Data Formats | INT8, BF16, FP16, TF32, FP32, FP64 | FP16, FP32, FP64 |
Compare Average On-Demand Pricing
| A30 | V100 | |
|---|---|---|
| 1 GPU | $0.35 /hr | $1.36 /hr |
| 2 GPUs | $0.70 /hr | $0.78 /hr |
| 4 GPUs | $1.40 /hr | N/A |
| 8 GPUs | $2.80 /hr | $4.72 /hr |
Frequently Asked Questions: A30 vs V100
The main differences are VRAM (24 GB vs 16 GB), FP16 throughput (10.32 vs 28.26 TFLOPS), architecture (Ampere vs Volta). The A30 uses the Ampere architecture while the V100 is based on Volta, giving each GPU different generational capabilities.
The V100 is generally better for large language model training due to its higher throughput and 16 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 V100 starts from $0.39/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The A30 has more VRAM at 24 GB, compared to 16 GB on the V100. 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 V100 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 V100 is currently available across 3 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 A30 or V100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A30 & V100 Instances
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