GTX 1070 Ti vs RTX 4080 SUPER

Compute performance shows a clear gap: the RTX 4080 SUPER delivers 52 TFLOPS in FP32 and FP16, compared to the GTX 1070 Ti's 8.9 TFLOPS, a roughly sixfold increase. This delta translates to faster model training and inference times, especially in FP16-optimized deep learning frameworks where the Ada Lovelace architecture leverages tensor cores absent in Pascal. Training large language models benefits from the higher throughput, reducing epochs from days to hours on equivalent datasets. Memory specifications further favor the RTX 4080 SUPER: 16 GB GDDR6X versus 8 GB GDDR5 supports larger batch sizes without out-of-memory errors, vital for fine-tuning transformers exceeding 7 billion parameters. Bandwidth at 736 GB/s versus 256 GB/s accelerates data transfers, minimizing bottlenecks in memory-bound operations like Stable Diffusion generation. Power draw reflects this: 320W TDP for the RTX 4080 SUPER demands robust cooling, while the GTX 1070 Ti's 180W suits lower-power setups.

The GTX 1070 Ti suits legacy on-premises deployments with existing Pascal-compatible infrastructure. Its 180W TDP enables operation in power-constrained environments, such as older desktops or small clusters, without upgrades. Light scientific computing or basic inference on models under 1 GB fits within 8 GB VRAM and 8.9 TFLOPS, avoiding cloud costs where no live offers exist.

Opt for the RTX 4080 SUPER in demanding AI workflows requiring cloud scalability. Access starts at $0.17 per hour, ideal for bursty LLM training or Stable Diffusion pipelines leveraging 52 TFLOPS and 16 GB VRAM. High bandwidth of 736 GB/s excels in large-batch inference, unavailable with the GTX 1070 Ti.