Rent NVIDIA GeForce RTX 3080 Ti in the Cloud

NVIDIA GeForce RTX 3080 Ti is built on the Ampere architecture and ships with 12 GB of GDDR6X memory at 912 GB/s bandwidth. Released in 2021, the card delivers 34.1 FP16 TFLOPS and 17 FP32 TFLOPS at a 350W TDP.

For machine learning researchers, those numbers translate into several practical limits: the VRAM ceiling dictates the largest large language model weights you can load (and the maximum batch size at a given sequence length), while memory bandwidth sets the upper bound for attention-heavy inference. Compute throughput matters most for dense matrix multiplications — pre-training, large-batch pre-training, and diffusion.

Check the NVIDIA GeForce RTX 3080 Ti page for complete specifications and related GPU matchups.

NVIDIA GeForce RTX 3080 Ti performance headline: 34.1 FP16 TFLOPS, 17 FP32 TFLOPS, 912 GB/s bandwidth, 12 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 912 GB/s figure. Diffusion image generation benchmarks sit between the two — compute-heavy steps utilise tensor cores well, while attention blocks still touch bandwidth.

See the NVIDIA GeForce RTX 3080 Ti page for the full spec sheet and comparisons to related GPUs.

NVIDIA GeForce RTX 3080 Ti shines for Gaming, inference. The card's 12 GB of VRAM and Ampere-generation tensor cores make it a strong match for AI AI workloads that push batch sizes hard and benefit from modern mixed-precision support.

It's overkill for toy experiments and under-sized for the largest frontier models, but covers the vast middle: fine-tuning open-source transformers, training diffusion models, running batch real-time serving pipelines, and supporting interactive workflows that need more VRAM than a consumer card offers.

Check the NVIDIA GeForce RTX 3080 Ti page for complete specifications and related GPU matchups.