NVIDIA A30
3,584
CUDA Cores
24GB
VRAM
933
GB/s
Technical Specifications
3,584
CUDA Cores
930
Base MHz
1440
Boost MHz
24GB HBM2
3072-bit bus
Performance
10.3
FP32 TFLOPS
165
FP16 TFLOPS
165W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Ampere (Unknown) |
| Release Date | 2021-04-12 |
| Launch Price | $5,000.00 |
| Process | 7nm |
| Transistors | 54.2B |
AI Features
Gen 3
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.5in
Length
4.4in
Width
2-slot
Height
About A30 GPU
The NVIDIA A30 was introduced in 2021 on the Ampere architecture. Its specification combines 3,584 CUDA cores with 24GB of HBM2, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 24GB memory capacity, the A30 is best evaluated for model fine-tuning, production inference, image generation, and multi-GPU development. Always size the model, optimizer state, KV cache, and framework overhead together rather than choosing a GPU from core count alone.
GPUvec does not currently have an active hourly cloud listing for the A30. This is not a zero-dollar offer: it means no comparable provider price is available in the present dataset. Check the provider directory for availability or compare a related GPU with an active listing.
For software planning, the A30 combines 933 GB/s of memory bandwidth, CUDA compute capability 8.0, and a 7nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
External Resources
Learn more about GPUs from these authoritative sources:
Official CUDA programming guide
NVIDIA GPU Specifications →Official NVIDIA GPU specs
TechPowerUp GPU Database →Comprehensive GPU specifications
CUDA Compute Capability Guide →GPU compute capability reference
What You Need to Know About the A30
Complete Specifications for the NVIDIA A30
The NVIDIA A30 uses the Ampere architecture and combines 3,584 CUDA cores with 24GB of HBM2. GPUvec records 933 GB/s memory bandwidth alongside architecture, release date, process node, accelerator features, and physical specifications so the page can be evaluated from source data instead of model-name assumptions.
Compare NVIDIA A30 Cloud Rental Prices per Hour
GPUvec currently has no comparable active hourly listing for this model; this means pricing is unavailable, not free. Provider rates can vary by region, host configuration, billing model, and availability. Use the provider directory and related-GPU links to compare an available alternative when this exact accelerator is not listed.
Is the NVIDIA A30 the Right GPU for Your AI Workload?
Start with the 24GB memory limit, then evaluate 933 GB/s memory bandwidth, compute features, and software compatibility for your workload. Model weights, KV cache, activations, optimizer state, and framework overhead all consume memory. Compare this page with related accelerators rather than assuming that a higher core count alone guarantees a better training or inference result.
Top GPUs for Training and Inference
| Category | Rank 1 | Rank 2 | Rank 3 |
|---|---|---|---|
| Best for Training | NVIDIA H200 | NVIDIA H100 | NVIDIA B200 |
| Best for Inference | NVIDIA A40 | NVIDIA A100 | NVIDIA A10 |
Compare GPU specifications and cloud instances to find the best GPU for your workload.