GPU NVIDIA

RTX 5080

Architecture · 16GB · PCIe

VRAM
16GB
FP16
-
TDP
-
Hardware Price
-
Cloud from
$0.590/hr
1 providers
Cheapest at RunPod →

Quick Insights

Performance/Dollar
N/A
FP16 performance per $1000
VRAM/Dollar
N/A
VRAM per $1000
vs null Average
N/A
FP16 TFLOPS comparison
Cloud Availability
1 providers
from $0.590/hr

Specifications

VRAM 16GB
Memory Bandwidth -
FP16 TFLOPS -
Tensor TFLOPS -
FP32 TFLOPS -
TDP -
Form Factor -
Architecture -
NVLink No
Release Date -

Cloud GPU Pricing

Rent RTX 5080 from 1 cloud providers. Prices shown per GPU per hour.

Provider Type Instance GPUs On-Demand Per GPU Spot Availability
RunPod gpu-cloud NVIDIA GeForce RTX 5080 1x $0.590/hr $0.590/hr Cheapest $0.300/hr (-49%) -
Best Spot Deal: RunPod offers spot pricing at $0.300/hr (49% off on-demand).

RTX 5080 vs Alternatives

Compare RTX 5080 with similar GPUs from other brands.

GPU VRAM FP16 TFLOPS Bandwidth Hardware Price Cloud Price
RTX 5080 Current 16GB - - - - -
AMD Radeon RX 7900 XT AMD 20GB (+25%) 104.0 800 GB/s - - Compare
AMD Radeon RX 7900 XTX AMD 24GB (+50%) 122.0 960 GB/s - - Compare
AMD Instinct MI100 AMD 32GB (+100%) 184.6 1.2 TB/s - - Compare

Best Use Cases

No specific use case recommendations for RTX 5080 yet.

Browse All Use Cases →

Compare RTX 5080

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Frequently Asked Questions about RTX 5080

Pricing for RTX 5080 varies. Check our cloud pricing section for rental options starting at $0.590/hr.

Yes, the RTX 5080 with 16GB VRAM is suitable for many AI/ML workloads. For large language models, you may need multiple GPUs or consider higher-VRAM options like A100 or H100.

Consider buying for long-term, heavy usage (>4 hrs/day). Rent from cloud providers for short-term projects, experimentation, or when you need to scale quickly.

With 16GB VRAM and - FP16 TFLOPS, the RTX 5080 can run: Stable Diffusion, smaller LLMs (7B quantized), deep learning training, and gaming at high settings.

The RTX 5080 offers 16GB VRAM and - FP16 performance at its price point. Compare with similar GPUs using our comparison tool above. Key factors: VRAM for model size, TFLOPS for speed, and price for budget.