NVIDIA RTX A4500
7,168
CUDA Cores
20GB
VRAM
640
GB/s
Technical Specifications
7,168
CUDA Cores
1050
Base MHz
1650
Boost MHz
20GB GDDR6
320-bit bus
Performance
23.7
FP32 TFLOPS
47.4
FP16 TFLOPS
200W
TDP
Cloud Availability
0
Available Instances
$0.00/hr
Starting Price
Detailed Specifications
| Architecture | Ampere (Unknown) |
| Release Date | 2024-01-15 |
| Launch Price | $1,599.00 |
| Process | 8nm |
| Transistors | 28.3B |
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 RTX A4500 GPU
The NVIDIA RTX A4500 was introduced in 2024 on the Ampere architecture. Its specification combines 7,168 CUDA cores with 20GB of GDDR6, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 20GB memory capacity, the RTX A4500 is best evaluated for quantized language-model inference, image generation, and medium-sized training jobs. 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 RTX A4500. 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 RTX A4500 combines 640 GB/s of memory bandwidth, CUDA compute capability the latest, and a 8nm 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 RTX A4500
Complete Specifications for the NVIDIA RTX A4500
The NVIDIA RTX A4500 uses the Ampere architecture and combines 7,168 CUDA cores with 20GB of GDDR6. GPUvec records 640 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 RTX A4500 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 RTX A4500 the Right GPU for Your AI Workload?
Start with the 20GB memory limit, then evaluate 640 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.