NVIDIA A100
6,912
CUDA Cores
40GB
VRAM
1555
GB/s
Technical Specifications
6,912
CUDA Cores
1410
Base MHz
1410
Boost MHz
40GB HBM2e
5120-bit bus
Performance
19.5
FP32 TFLOPS
312
FP16 TFLOPS
250W
TDP
Cloud Availability
11
Available Instances
$0.66/hr
Starting Price
Detailed Specifications
| Architecture | Ampere (Unknown) |
| Release Date | 2020-05-14 |
| Launch Price | $10,000.00 |
| 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 A100 GPU
The NVIDIA A100 was introduced in 2020 on the Ampere architecture. Its specification combines 6,912 CUDA cores with 40GB of HBM2e, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 40GB memory capacity, the A100 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 currently tracks 11 cloud listings for the A100, with the lowest observed hourly rate at $0.66. Prices and availability can change by provider, region, and instance configuration, so verify the final rate before starting a workload.
For software planning, the A100 combines 1,555 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.
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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 A100
Complete Specifications for the NVIDIA A100
The NVIDIA A100 uses the Ampere architecture and combines 6,912 CUDA cores with 40GB of HBM2e. GPUvec records 1,555 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 A100 Cloud Rental Prices per Hour
GPUvec currently tracks 11 cloud listings, with an observed starting rate of $0.66 per hour. 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 A100 the Right GPU for Your AI Workload?
Start with the 40GB memory limit, then evaluate 1,555 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.