NVIDIA RTX 5080
10,752
CUDA Cores
16GB
VRAM
960
GB/s
Technical Specifications
10,752
CUDA Cores
2295
Base MHz
2617
Boost MHz
16GB GDDR7
256-bit bus
Performance
56
FP32 TFLOPS
112
FP16 TFLOPS
360W
TDP
Cloud Availability
1
Available Instances
$0.20/hr
Starting Price
Detailed Specifications
| Architecture | Blackwell (Unknown) |
| Release Date | 2025-01-30 |
| Launch Price | $999.00 |
| Process | 4nm |
| Transistors | 45.6B |
AI Features
Gen 5
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
12.0in
Length
4.5in
Width
3-slot
Height
About RTX 5080 GPU
The NVIDIA RTX 5080 was introduced in 2025 on the Blackwell architecture. Its specification combines 10,752 CUDA cores and 336 Tensor Cores with 16GB of GDDR7, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 16GB memory capacity, the RTX 5080 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 currently tracks 1 cloud listing for the RTX 5080, with the lowest observed hourly rate at $0.20. Prices and availability can change by provider, region, and instance configuration, so verify the final rate before starting a workload.
For software planning, the RTX 5080 combines 960 GB/s of memory bandwidth, CUDA compute capability the latest, and a 4nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
The RTX 5080 uses Blackwell architecture and 16GB of high-bandwidth GDDR7 memory. Its 960 GB/s memory bandwidth is a notable advantage for inference and generative workloads, although 16GB still sets a clear capacity boundary for large unquantized models.
When comparing the RTX 5080 with 24GB cards, separate throughput from capacity. Its Blackwell compute and 960 GB/s bandwidth can accelerate a workload that fits in 16GB, but they cannot compensate when model weights, cache, and framework overhead exceed that memory ceiling.
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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 RTX 5080
Complete Specifications for the NVIDIA RTX 5080
The NVIDIA RTX 5080 uses the Blackwell architecture and combines 10,752 CUDA cores with 16GB of GDDR7. GPUvec records 960 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 5080 Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.20 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 RTX 5080 the Right GPU for Your AI Workload?
Start with the 16GB memory limit, then evaluate 960 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.