Rent NVIDIA GeForce GTX 1080 in the Cloud

NVIDIA GeForce GTX 1080 is a Pascal card with 8 GB of GDDR5X memory, 320 GB/s of bandwidth, and TFLOPS of FP16 compute. It launched in 2016 and uses up to 180W of power.

On real workloads the memory subsystem is typically the most important factor: 320 GB/s of bandwidth is enough to sustain most transformer decoding tasks without starving the compute units, while 8 GB of VRAM supports a wide range of model sizes, especially once quantisation is applied.

Check the NVIDIA GeForce GTX 1080 page for complete specifications and related GPU matchups.

FP16 TFLOPS and 320 GB/s of memory bandwidth put NVIDIA GeForce GTX 1080 squarely in the class of accelerators targeted at modern transformer workloads. FP32 caps at 8.9 TFLOPS, which still handles most non-AI scientific compute comfortably.

For training from scratch, token throughput roughly tracks FP16 TFLOPS. For production inference on foundation models, throughput tracks bandwidth. Real-world numbers will depend heavily on the framework stack (PyTorch, TensorRT-LLM, vLLM), and can vary 30-50% depending on how aggressively you quantise.

See the NVIDIA GeForce GTX 1080 page for the full spec sheet and comparisons to related GPUs.

NVIDIA GeForce GTX 1080 is best for workloads where its 8 GB VRAM and Pascal tensor cores are well-matched: Legacy inference, experimentation.

If your workload needs significantly more memory (e.g., training frontier-scale models from scratch), NVIDIA GeForce GTX 1080 is undersized and you'd want an H100/H200/B200 class card. If your workload needs less (e.g., small-scale serving on 7B-parameter models), cheaper cards like L4 or RTX 4090 may be more cost-efficient. For the middle band, NVIDIA GeForce GTX 1080 is usually the sensible pick.

Check the NVIDIA GeForce GTX 1080 page for complete specifications and related GPU matchups.