A30 vs A10
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.
A10
The NVIDIA A10 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A30 vs A10: Which Should You Choose?
Both the A30 and A10 offer 24 GB of VRAM, putting them on equal footing for memory-bound workloads. On FP16 throughput, the A10 delivers 31.24 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 0.60 TB/s on the A10, which directly impacts inference latency for memory-bandwidth-bound models. On Shadeform, the A30 starts from $0.35/hr versus $1.29/hr for the A10 — 269% more expensive — reflecting the performance premium.
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:
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
A10 — 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 A10 when:
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
See how the A30 & A10 compare
Compare detailed hardware specifications and average pricing for the A30 and A10.
Compare Hardware Specifications
| A30 | A10 | |
|---|---|---|
| GPU Type | A30 | A10 |
| VRAM per GPU | 24 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Ampere |
| Interconnect | PCIe Gen4 | PCIe Gen4 |
| Memory Bandwidth | 933 GB/s | 600 GB/s |
| FP16 TFLOPS | 10.32 TFLOPS (1:1) | 31.24 TFLOPS (1:1) |
| CUDA Cores | 3584 | 9216 |
| Tensor Cores | 224 (3rd Gen) | 288 (3rd Gen) |
| RT Cores | N/A | 72 (2nd Gen) |
| Base Clock | 930 MHz | 885 MHz |
| Boost Clock | 1440 MHz | 1695 MHz |
| TDP | 165W | 150W |
| Process Node | TSMC 7nm | TSMC 8nm |
| Data Formats | INT8, BF16, FP16, TF32, FP32, FP64 | INT4, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| A30 | A10 | |
|---|---|---|
| 1 GPU | $0.35 /hr | $1.29 /hr |
| 2 GPUs | $0.70 /hr | N/A |
| 4 GPUs | $1.40 /hr | N/A |
| 8 GPUs | $2.80 /hr | N/A |
Frequently Asked Questions: A30 vs A10
The main differences are FP16 throughput (10.32 vs 31.24 TFLOPS).
The A10 is generally better for large language model training due to its higher throughput and 24 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 A10 starts from $1.29/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
Based on TFLOPS per dollar, the A30 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 A10, paying the premium may be justified by faster job completion and lower total cost.
The A30 is currently available across 1 cloud providers on Shadeform's network, compared to 1 for the A10. 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 A10. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A30 & A10 Instances
Browse available instances with A30 and A10 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
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