NVIDIA RTX 3060
3,584
CUDA Cores
12GB
VRAM
360
GB/s
Technical Specifications
3,584
CUDA Cores
1320
Base MHz
1777
Boost MHz
12GB GDDR6
192-bit bus
Performance
12.7
FP32 TFLOPS
25.5
FP16 TFLOPS
170W
TDP
Cloud Availability
1
Available Instances
$0.06/hr
Starting Price
Detailed Specifications
| Architecture | Ampere (Unknown) |
| Release Date | 2021-02-25 |
| Launch Price | $329.00 |
| Process | 8nm |
| Transistors | 12.0B |
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 3060 GPU
The NVIDIA RTX 3060 was introduced in 2021 on the Ampere architecture. Its specification combines 3,584 CUDA cores and 112 Tensor Cores with 12GB of GDDR6, which defines both the workloads it can run and the batch sizes it can hold.
Based on its 12GB memory capacity, the RTX 3060 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 3060, with the lowest observed hourly rate at $0.06. 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 3060 combines 360 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 3060 pairs Ampere features with 12GB of GDDR6 memory, giving it more model capacity than many faster 8GB cards. That trade-off can make it useful for budget inference and development workloads where fitting the model matters more than maximum throughput.
For the RTX 3060, the key buying or rental question is whether 12GB prevents offloading that an 8GB card would require. Its 360 GB/s bandwidth is lower than several higher-tier Ampere cards, so it is commonly a capacity-first choice rather than the fastest option for a model that already fits elsewhere.
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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 3060
Complete Specifications for the NVIDIA RTX 3060
The NVIDIA RTX 3060 uses the Ampere architecture and combines 3,584 CUDA cores with 12GB of GDDR6. GPUvec records 360 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 3060 Cloud Rental Prices per Hour
GPUvec currently tracks 1 cloud listing, with an observed starting rate of $0.06 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 3060 the Right GPU for Your AI Workload?
Start with the 12GB memory limit, then evaluate 360 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.