NVIDIA A10 vs NVIDIA A30 GPU comparison
A practical comparison of NVIDIA A10 and NVIDIA A30 for different GPU workload requirements.
1. Product positioning
A10 is a general-purpose GPU for AI inference, graphics and video. A30 focuses on compute workloads, including AI and HPC. In simple terms, the article compares A10 to a server-oriented RTX GPU and A30 to an AI compute GPU closer to A100.
2. Main specifications compared
| Specification | NVIDIA A10 | NVIDIA A30 |
|---|---|---|
| Architecture | Ampere GA102 | Ampere GA100, as in A100 |
| VRAM | 24GB GDDR6 | 24GB HBM2 |
| Memory bandwidth | 600 GB/s | 933 GB/s |
| FP32 | 31.2 TFLOPS | 10.3 TFLOPS |
| FP16 Tensor | 125 TFLOPS | 165 TFLOPS |
| FP64 | Listed as unsupported in the source article | 5.2 TFLOPS |
| INT8 | 250 TOPS | 330 TOPS |
| TDP | 150W | 165W |
| NVLink | No | Yes |
| MIG | No | Yes |
3. Core differences
Architecture
A30 uses GA100, like A100, and is oriented toward AI training and HPC. A10 uses GA102, also associated with RTX 3080/3090, and emphasizes graphics and inference. The article positions A30 for AI and scientific compute, and A10 for rendering and FP32 workloads.
Memory
A30 combines HBM2 with 933 GB/s bandwidth; A10 uses GDDR6 at 600 GB/s. The higher bandwidth favors large models and training, while memory-heavy workloads may bottleneck on A10.
Compute
A10 has strong FP32 performance, around 31 TFLOPS, for graphics. A30 emphasizes Tensor and FP64 workloads for AI, simulation and scientific computing. The source comparison does not recommend A10 for FP64-oriented HPC.
Virtualization and multiple instances
A30 supports MIG, allowing a GPU to be divided into instances. A10 does not support MIG. This makes A30 relevant to cloud GPU and multi-tenant AI deployments.
4. Suggested workloads
NVIDIA A10
- VDI and cloud workstations using vGPU.
- 3D rendering with Blender or Unreal, CAD and design.
- Video encoding and decoding.
- Light AI inference.
The article associates A10 with graphics, AI inference and cloud workstations.
NVIDIA A30
- AI training, including Transformers, computer vision and NLP.
- Large-scale inference.
- HPC, simulation, scientific computing and data analytics.
The article associates A30 with AI inference and enterprise compute workloads.
5. Quick selection guide
| Need | Suggested GPU |
|---|---|
| AI training or large-scale inference | A30 |
| Rendering, graphics or VDI | A10 |
| HPC and simulation | A30 |
| Lower cost | A10 |
| Multi-tenant GPU | A30 |
6. Conclusion
Choose A10 when building GPU cloud or VDI services, combining AI and graphics, or prioritizing cost. Choose A30 for demanding training and inference, higher memory bandwidth and large LLM or computer-vision workloads.
The original article advertises A10, A30, L4 and A100 options from FPT SMART CLOUD and invites readers to contact the team for configuration advice. Confirm current options against the published catalog and a quotation.
Published ; updated