Economical inference
NVIDIA A2
Ampere · 16 GB PCIe
Memory
16 GB GDDR6
16 GB GDDR6
Bandwidth
200 GB/s
200 GB/s
FP32
4.5 TFLOPS
4.5 TFLOPS
TDP
40–60W
40–60W
Independently verified GPU comparison Updated July 15, 2025
NVIDIA A2 vs A30
Architecture, memory, AI performance, workload fit and rental pricing compared using NVIDIA’s official specifications and AceCloud’s current cloud GPU configurations.
Balanced AI and HPC
NVIDIA A30
Ampere · 24 GB PCIe
Memory
24 GB HBM2
24 GB HBM2
Bandwidth
933 GB/s
933 GB/s
FP32
10.3 TFLOPS
10.3 TFLOPS
TDP
165W
165W
The honest take: A2 is the economical choice for lightweight inference, video analytics, virtual workstations and development workloads. A30 costs about 2.6× more but provides 1.5× the memory, 4.7× the bandwidth and over 9× the sparse FP16 Tensor performance. It is better suited to model training, heavier inference, HPC and multi-tenant GPU environments.
Quick answer
Which GPU fits your workload?
A workload-first view of where A2 reduces inference costs and where A30 delivers greater compute and memory performance.
Lightweight AI inference
Compact vision, recommender and NLP models within 16 GB
A2 For smaller vision, classification, recommendation and NLP models, A2 provides useful Tensor Core acceleration at a substantially lower monthly price. It is a practical option when the workload fits within 16 GB and does not require high memory bandwidth.
AI training and fine-tuning
Training workloads needing 24 GB HBM2 and higher Tensor throughput
A30 A30 offers 24 GB HBM2, 933 GB/s bandwidth and substantially higher TF32, BF16 and FP16 performance. It is better equipped for training and fine-tuning models that would run slowly or exceed the memory limits of A2.
Intelligent video analytics
Smart-camera inference, media pipelines and AV1 decoding
A2 A2 includes one video encoder and two video decoders with AV1 decode support. It is a strong fit for efficient video inference, smart-camera analytics and media pipelines that need hardware encoding or AV1 decoding.
High-throughput inference
Large batches and production services requiring higher INT8 throughput
A30 A30 delivers up to 661 INT8 TOPS with sparsity, compared with 72 INT8 TOPS on A2. It is better suited to production inference services that require larger batches or substantially higher throughput.
HPC and scientific computing
FP64 simulations, scientific workloads and GPU-accelerated analytics
A30 A30 supports FP64 and FP64 Tensor Core computation, HBM2 memory and NVLink. These capabilities make it the clear choice for simulations, scientific computing and GPU-accelerated analytics.
Virtual desktops and lightweight visualization
Virtualized graphics and lightweight visualization
A2 A2 includes 10 RT Cores and supports NVIDIA vPC, vApps and RTX Virtual Workstation software. Its low-profile form factor and lower power profile make it suitable for virtualized graphics and lightweight visualization workloads.
Side by side
Full technical specifications
Every technical figure below is based on NVIDIA’s official A2 and A30 specifications.
NVIDIA A100 and NVIDIA H100 technical specification comparison
| Specification | NVIDIA A2 | NVIDIA A30 |
|---|---|---|
| Architecture | NVIDIA Ampere | NVIDIA Ampere |
| GPU memory | 16 GB GDDR6 | 24 GB HBM2 |
| Memory bandwidth | 200 GB/s | 933 GB/s |
| FP64 | Not specified | 5.2 TFLOPS |
| FP64 Tensor Core | Not specified | 10.3 TFLOPS |
| FP32 | 4.5 TFLOPS | 10.3 TFLOPS |
| TF32 Tensor Core, dense | 9 TFLOPS | 82 TFLOPS |
| TF32 Tensor Core, sparse | 18 TFLOPS | 165 TFLOPS |
| BF16 Tensor Core, dense | 18 TFLOPS | 165 TFLOPS |
| BF16 Tensor Core, sparse | 36 TFLOPS | 330 TFLOPS |
| FP16 Tensor Core, dense | 18 TFLOPS | 165 TFLOPS |
| FP16 Tensor Core, sparse | 36 TFLOPS | 330 TFLOPS |
| INT8 Tensor Core, dense | 36 TOPS | 330 TOPS |
| INT8 Tensor Core, sparse | 72 TOPS | 661 TOPS |
| INT4 Tensor Core, dense | 72 TOPS | 661 TOPS |
| INT4 Tensor Core, sparse | 144 TOPS | 1,321 TOPS |
| RT Cores | 10 | Not listed |
| Video encoders | 1 | Not listed |
| Video decoders | 2, including AV1 decode | 4 |
| Additional media engines | Not listed | 1 OFA and 1 JPEG decoder |
| MIG support | No | Up to 4 GPU instances |
| NVLink | No | Third-generation NVLink, 200 GB/s |
| PCIe interface | PCIe Gen4 x8 | PCIe Gen4, 64 GB/s |
| Form factor | Single-slot, low-profile PCIe | Dual-slot, full-height, full-length |
| Maximum TDP | 40–60W, configurable | 165W |
| AceCloud configurations | 1× and 2× GPUs | 1× and 2× GPUs |
A30 costs approximately 2.6× more per month, but delivers over 9× the theoretical FP16 and INT8 Tensor performance with sparsity. If the workload can use that additional throughput, A30 may provide a lower cost per completed job or inference despite its higher monthly rate.
Straight comparison
How much faster is A30, really?
Raw specifications translated into simple relative performance. Longer bars indicate higher output.
FP16 Tensor performance, with sparsity
TFLOPS A2
1.0×
A30
9.2×
INT8 Tensor performance, with sparsity
TOPS A2
1.0×
A30
9.2x
Memory bandwidth
GB/s A2
1.0×
A30
4.7×
GPU memory capacity
GB A2
1.0×
A30
1.5×
FP32 performance
TFLOPS A2
1.0×
A30
2.3×
Monthly rental price
₹ A2
1.0×
A30
2.6×
Disclaimer: These are theoretical peak specifications. Real-world performance depends on model architecture, precision, batch size, framework, optimization, CPU and RAM allocation and storage throughput.
What you pay
NVIDIA A2 vs A30 rental pricing
Current entry-level Noida pricing for Linux instances, excluding taxes:
NVIDIA A2 16 GB
1× A2 cloud GPU instance
₹
15,693
/month
15,693
/month
- 8 vCPUs
- 32 GB RAM
- Six-month effective price: ₹14,908/month
- Twelve-month effective price: ₹14,124/month
₹20,000 free credits · Spin up in <15 min
NVDIA A30 24 GB
1× A30 cloud GPU instance
₹
40,693
/month
40,693
/month
- 8 vCPUs
- 32 GB RAM
- Six-month effective price: ₹38,658/month
- Twelve-month effective price: ₹36,624/month
₹20,000 free credits · Help with setup
A30 costs approximately 2.6× more per month, but delivers over 9× the theoretical FP16 and INT8 Tensor performance with sparsity. If the workload can use that additional throughput, A30 may provide a lower cost per completed job or inference despite its higher monthly rate.
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