NVIDIA RTX 4070 Ti
7,680
CUDA Cores
12GB
VRAM
504
GB/s
Technical Specifications
7,680
CUDA Cores
2310
Base MHz
2610
Boost MHz
12GB GDDR6X
192-bit bus
Performance
40.1
FP32 TFLOPS
80.2
FP16 TFLOPS
285W
TDP
Cloud Availability
1
Available Instances
$0.10/hr
Starting Price
Detailed Specifications
| Architecture | Ada Lovelace (Unknown) |
| Release Date | 2023-01-05 |
| Launch Price | $799.00 |
| Process | 5nm |
| Transistors | 35.8B |
AI Features
Gen 4
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.2in
Length
4.4in
Width
3-slot
Height
About RTX 4070 Ti GPU
The NVIDIA RTX 4070 Ti was introduced in 2023 on the Ada Lovelace architecture. Its specification combines 7,680 CUDA cores and 240 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 Ti 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 Ti, 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 Ti 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 Ti offers 7,680 CUDA cores, 12GB of GDDR6X memory, and Ada Lovelace Tensor Cores. It targets high-throughput inference and creative workloads, but users comparing it with 16GB or 24GB cards should account for the smaller model and batch-size ceiling.
For jobs that fit within 12GB, the RTX 4070 Ti's 7,680 CUDA cores can provide a meaningful throughput gain over the RTX 4070. For jobs that do not fit, both cards reach the same capacity boundary, so compare the Ti against 16GB and 24GB alternatives rather than assuming more cores resolve an out-of-memory error.
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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 Ti
Complete Specifications for the NVIDIA RTX 4070 Ti
The NVIDIA RTX 4070 Ti uses the Ada Lovelace architecture and combines 7,680 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 Ti 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 Ti 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 |
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