NVIDIA RTX 4070
5,888
CUDA Cores
12GB
VRAM
504
GB/s
Technical Specifications
5,888
CUDA Cores
1920
Base MHz
2475
Boost MHz
12GB GDDR6X
192-bit bus
Performance
29.1
FP32 TFLOPS
58.2
FP16 TFLOPS
200W
TDP
Cloud Availability
1
Available Instances
$0.10/hr
Starting Price
Detailed Specifications
| Architecture | Ada Lovelace (Unknown) |
| Release Date | 2023-04-13 |
| Launch Price | $599.00 |
| Process | 5nm |
| Transistors | 35.8B |
AI Features
Gen 4
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
9.6in
Length
4.4in
Width
2-slot
Height
About RTX 4070 GPU
The NVIDIA RTX 4070 was introduced in 2023 on the Ada Lovelace architecture. Its specification combines 5,888 CUDA cores and 184 Tensor Cores with 12GB of GDDR6X, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 12GB memory capacity, the RTX 4070 is best evaluated for budget inference, computer vision, local development, and carefully sized fine-tuning 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 4070, with the lowest observed hourly rate at $0.10. 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 4070 combines 504 GB/s of memory bandwidth, CUDA compute capability 8.9, and a 5nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
The RTX 4070 combines Ada Lovelace efficiency with 12GB of GDDR6X memory. It is a sensible option for local inference, image generation, and development workloads that benefit from newer Tensor Core support without the cost or power requirements of a higher-tier card.
The RTX 4070 and RTX 4070 Ti both expose 12GB and 504 GB/s bandwidth, but the Ti model has 7,680 CUDA cores versus 5,888. The non-Ti card is therefore the value-oriented comparison point: measure whether the Ti's additional compute shortens runtime enough to justify its provider rate.
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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 4070
Complete Specifications for the NVIDIA RTX 4070
The NVIDIA RTX 4070 uses the Ada Lovelace architecture and combines 5,888 CUDA cores with 12GB of GDDR6X. GPUvec records 504 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 4070 Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.10 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 4070 the Right GPU for Your AI Workload?
Start with the 12GB memory limit, then evaluate 504 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.