NVIDIA RTX 2080
2,944
CUDA Cores
8GB
VRAM
448
GB/s
Technical Specifications
2,944
CUDA Cores
1515
Base MHz
1710
Boost MHz
8GB GDDR6
256-bit bus
Performance
10.1
FP32 TFLOPS
20.1
FP16 TFLOPS
215W
TDP
Cloud Availability
1
Available Instances
$0.06/hr
Starting Price
Detailed Specifications
| Architecture | Turing (Unknown) |
| Release Date | 2018-09-20 |
| Launch Price | $699.00 |
| Process | 12nm |
| Transistors | 13.6B |
AI Features
Gen 2
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.5in
Length
4.4in
Width
2-slot
Height
About RTX 2080 GPU
The NVIDIA RTX 2080 was introduced in 2018 on the Turing architecture. Its specification combines 2,944 CUDA cores and 368 Tensor Cores with 8GB of GDDR6, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 8GB memory capacity, the RTX 2080 is best evaluated for compact inference, rendering, computer vision, and legacy CUDA workloads. 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 2080, with the lowest observed hourly rate at $0.06. 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 2080 combines 448 GB/s of memory bandwidth, CUDA compute capability the latest, and a 12nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
Compared with the RTX 2070, the RTX 2080 raises the CUDA and Tensor Core counts while retaining the same 8GB memory ceiling. It is best evaluated for legacy inference, rendering, and compact computer-vision jobs rather than current large-model training.
The RTX 2080 has 2,944 CUDA cores and 368 Tensor Cores versus the RTX 2070's 2,304 and 288, respectively. Both share 8GB and 448 GB/s bandwidth, so the 2080 improves compute throughput without solving memory-fit failures. Compare measured runtime, not just hourly price, when both are available.
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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 2080
Complete Specifications for the NVIDIA RTX 2080
The NVIDIA RTX 2080 uses the Turing architecture and combines 2,944 CUDA cores with 8GB of GDDR6. GPUvec records 448 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 2080 Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.06 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 2080 the Right GPU for Your AI Workload?
Start with the 8GB memory limit, then evaluate 448 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.