NVIDIA RTX 3070
5,888
CUDA Cores
8GB
VRAM
448
GB/s
Technical Specifications
5,888
CUDA Cores
1500
Base MHz
1725
Boost MHz
8GB GDDR6
256-bit bus
Performance
20.3
FP32 TFLOPS
40.6
FP16 TFLOPS
220W
TDP
Cloud Availability
2
Available Instances
$0.07/hr
Starting Price
Detailed Specifications
| Architecture | Ampere (Unknown) |
| Release Date | 2020-10-29 |
| Launch Price | $499.00 |
| Process | 8nm |
| Transistors | 17.4B |
AI Features
Gen 3
Tensor Cores
Disabled
Transformer Engine
Not Supported
Flash Attention
Physical Specifications
Dimensions
9.5in
Length
4.4in
Width
2-slot
Height
About RTX 3070 GPU
The NVIDIA RTX 3070 was introduced in 2020 on the Ampere architecture. Its specification combines 5,888 CUDA cores and 184 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 3070 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 2 cloud listings for the RTX 3070, 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 combines 448 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 provides substantially more CUDA throughput than entry-level Ampere cards, but its 8GB memory capacity is the practical constraint for AI. It suits compact inference and vision pipelines; larger checkpoints generally require quantization, offloading, or another GPU.
Choose the RTX 3070 when a workload fits comfortably within 8GB and benefits from its 5,888 CUDA cores. If memory use approaches the limit, the 12GB RTX 3060 or a newer 12GB card may complete the job more reliably even when its theoretical compute throughput is lower.
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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
Complete Specifications for the NVIDIA RTX 3070
The NVIDIA RTX 3070 uses the Ampere architecture and combines 5,888 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 3070 Cloud Rental Prices per Hour
GPUvec currently tracks 2 cloud listings, 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 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.