NVIDIA RTX 3070 Ti
6,144
CUDA Cores
8GB
VRAM
608
GB/s
Technical Specifications
6,144
CUDA Cores
1575
Base MHz
1770
Boost MHz
8GB GDDR6X
256-bit bus
Performance
21.7
FP32 TFLOPS
43.4
FP16 TFLOPS
290W
TDP
Cloud Availability
1
Available Instances
$0.07/hr
Starting Price
Detailed Specifications
| Architecture | Ampere (Unknown) |
| Release Date | 2021-06-10 |
| Launch Price | $599.00 |
| Process | 8nm |
| Transistors | 17.4B |
AI Features
Gen 3
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
10.0in
Length
4.4in
Width
2-slot
Height
About RTX 3070 Ti GPU
The NVIDIA RTX 3070 Ti was introduced in 2021 on the Ampere architecture. Its specification combines 6,144 CUDA cores and 192 Tensor Cores with 8GB of GDDR6X, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 8GB memory capacity, the RTX 3070 Ti 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 3070 Ti, with the lowest observed hourly rate at $0.07. 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 3070 Ti combines 608 GB/s of memory bandwidth, CUDA compute capability the latest, and a 8nm manufacturing process. Confirm the minimum CUDA, PyTorch, TensorFlow, or driver version required by your application before renting or purchasing hardware.
The RTX 3070 Ti combines 6,144 CUDA cores with 608 GB/s of GDDR6X bandwidth, but only 8GB of VRAM. It remains capable for compute-heavy compact models and rendering, while memory capacity—not raw bandwidth—is the main limitation for modern AI workloads.
The RTX 3070 Ti's 608 GB/s bandwidth is substantially higher than the RTX 3070's 448 GB/s, but both retain 8GB. It is most compelling when the workload is bandwidth-sensitive and already proven to fit; otherwise, a larger-memory alternative is the safer comparison.
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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 3070 Ti
Complete Specifications for the NVIDIA RTX 3070 Ti
The NVIDIA RTX 3070 Ti uses the Ampere architecture and combines 6,144 CUDA cores with 8GB of GDDR6X. GPUvec records 608 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 3070 Ti Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.07 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 3070 Ti the Right GPU for Your AI Workload?
Start with the 8GB memory limit, then evaluate 608 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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