NVIDIA RTX 2070
2,304
CUDA Cores
8GB
VRAM
448
GB/s
Technical Specifications
2,304
CUDA Cores
1410
Base MHz
1620
Boost MHz
8GB GDDR6
256-bit bus
Performance
7.5
FP32 TFLOPS
14.9
FP16 TFLOPS
175W
TDP
Cloud Availability
1
Available Instances
$0.03/hr
Starting Price
Detailed Specifications
| Architecture | Turing (Unknown) |
| Release Date | 2018-10-17 |
| Launch Price | $499.00 |
| Process | 12nm |
| Transistors | 10.8B |
AI Features
Gen 2
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
9.0in
Length
4.4in
Width
2-slot
Height
About RTX 2070 GPU
The NVIDIA RTX 2070 was introduced in 2018 on the Turing architecture. Its specification combines 2,304 CUDA cores and 288 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 2070 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 2070, with the lowest observed hourly rate at $0.03. 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 2070 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.
The RTX 2070 is a first-generation Turing card with 8GB of GDDR6 memory. Its value today is compatibility with older CUDA workloads and inexpensive experimentation, while the 8GB limit makes it unsuitable for memory-heavy model training.
A practical RTX 2070 deployment should begin with an 8GB memory test using the exact precision and batch size planned for production. If the workload spills to system memory, compare the 12GB RTX 3060 before paying for additional 2070 instances; capacity can matter more than the older card's raw speed.
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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 2070
Complete Specifications for the NVIDIA RTX 2070
The NVIDIA RTX 2070 uses the Turing architecture and combines 2,304 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 2070 Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.03 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 2070 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.