Key Specifications
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Frequently Asked Questions
Why choose the RTX 2060 Super?
8GB GDDR6 with Turing Tensor Cores. Very low cost for basic GPU experimentation.
When is the RTX 2060 Super not a good fit?
8GB VRAM and Turing limitations make it a poor fit for modern AI workloads. Better suited for basic experimentation.
Are RTX 2060 Super prices going up or down?
On-demand pricing has remained stable, averaging around $0.10/hr per GPU.
What size AI models can the RTX 2060 Super run?
With 8GB of VRAM, the RTX 2060 Super is mainly limited to small quantized models and lightweight GPU workloads.
How much VRAM does the RTX 2060 Super have?
The RTX 2060 Super has 8GB of VRAM. Multi-GPU setups increase total memory, but that memory is not automatically pooled across GPUs.
What is the RTX 2060 Super's memory bandwidth?
The RTX 2060 Super has 448 GB/s of memory bandwidth. Higher bandwidth helps with faster data transfer between GPU memory and compute cores.
What data types does the RTX 2060 Super support?
The RTX 2060 Super supports 3 precision formats. Training: FP16, FP32. Inference: INT8.
Does the RTX 2060 Super support NVLink?
No. The RTX 2060 Super is a PCIe-only GPU with no NVLink, so it is better suited to single-GPU inference and smaller-scale workloads than large distributed training jobs.
Technical Specifications
| Architecture | Turing |
| CUDA Cores | 2176 |
| Memory Size | 8 GB |
| Memory Type | GDDR6 |
| Memory Bandwidth | 448 GB/s |
| TDP | 175W |
Alternatives to Nvidia RTX 2060 Super
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