Rent NVIDIA GeForce RTX 4080 SUPER in the Cloud

Short version of the NVIDIA GeForce RTX 4080 SUPER spec sheet: 16 GB GDDR6X, 736 GB/s, 52.4 FP16 TFLOPS, 26.2 FP32 TFLOPS, Ada Lovelace (2024), 320W.

Long version: the card is tuned for mixed-precision matrix multiplication on large tensors, which is exactly what transformer training and production inference demand. Bandwidth is generous enough to avoid stalling on attention operations, and VRAM capacity covers modern model sizes without requiring offloading to CPU memory.

The NVIDIA GeForce RTX 4080 SUPER page has the complete datasheet and side-by-side comparisons.

NVIDIA GeForce RTX 4080 SUPER performance specs: 52.4 FP16 TFLOPS / 26.2 FP32 TFLOPS / 736 GB/s / 16 GB.

Three workload classes, three different bottlenecks: fine-tuning stresses FP16/BF16 tensor cores (FLOPS-bound); serving on large language models stresses memory bandwidth (bandwidth-bound); HPC-style simulation stresses FP32 (also FLOPS-bound). NVIDIA GeForce RTX 4080 SUPER covers all three competently for its generation. If your workload mix leans toward one class, benchmark with that workload specifically rather than relying on synthetic peak numbers.

Review full specs and related comparisons on the NVIDIA GeForce RTX 4080 SUPER page.

Use cases where NVIDIA GeForce RTX 4080 SUPER performs well: Gaming, inference, fine-tuning. The card's 16 GB of VRAM, Ada Lovelace tensor cores, and consumer positioning suit it for AI teams who need cloud-grade hardware without the premium of the newest accelerators.

A good rule of thumb: if your model fits in 16 GB of VRAM at your target precision, and your workload is bandwidth- or compute-bound rather than memory-bound, NVIDIA GeForce RTX 4080 SUPER is likely a better economic choice than upgrading to a higher tier.

Full specs, benchmarks, and comparisons are on the NVIDIA GeForce RTX 4080 SUPER page.