Rent NVIDIA GeForce RTX 3090 Ti in the Cloud

Short version of the NVIDIA GeForce RTX 3090 Ti spec sheet: 24 GB GDDR6X, 1,008 GB/s, 40 FP16 TFLOPS, 20 FP32 TFLOPS, Ampere (2022), 450W.

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.

Review full specs and related comparisons on the NVIDIA GeForce RTX 3090 Ti page.

NVIDIA GeForce RTX 3090 Ti performance headline: 40 FP16 TFLOPS, 20 FP32 TFLOPS, 1,008 GB/s bandwidth, 24 GB VRAM.

Converted into practical benchmarks: model training a 7B-parameter LLM in FP16 with reasonable batch sizes typically saturates compute before bandwidth; real-time serving on the same model is usually bandwidth-bound and tracks the 1,008 GB/s figure. Diffusion image generation benchmarks sit between the two — compute-heavy steps utilise tensor cores well, while attention blocks still touch bandwidth.

Review full specs and related comparisons on the NVIDIA GeForce RTX 3090 Ti page.

Use cases where NVIDIA GeForce RTX 3090 Ti performs well: Training, fine-tuning, rendering. The card's 24 GB of VRAM, Ampere 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 24 GB of VRAM at your target precision, and your workload is bandwidth- or compute-bound rather than memory-bound, NVIDIA GeForce RTX 3090 Ti is likely a better economic choice than upgrading to a higher tier.

Review full specs and related comparisons on the NVIDIA GeForce RTX 3090 Ti page.