Rent NVIDIA GeForce RTX 3080 in the Cloud

NVIDIA GeForce RTX 3080 packs 10 GB of GDDR6X with 760 GB/s of bandwidth, delivers 29.8 TFLOPS of FP16 compute, and operates at 320W. It's based on the Ampere architecture introduced in 2020.

For most AI use cases this matters in three ways: 1) how big a model you can load; 2) how fast attention runs (bandwidth-bound); 3) how fast MatMul-heavy ops run (compute-bound). NVIDIA GeForce RTX 3080 tends to sit in a balanced spot on all three — which is why it shows up repeatedly in cloud catalogues aimed at ML teams.

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

Raw compute on NVIDIA GeForce RTX 3080 peaks at 29.8 FP16 TFLOPS and 14.9 FP32 TFLOPS, with 760 GB/s of memory bandwidth feeding the compute units. The Ampere architecture brings tensor cores optimised for BF16/FP16 / FP8 mixed precision — the formats that matter most for modern transformers.

Real-world model training throughput scales close to theoretical peaks on large batch sizes; smaller batches are memory-bound. For low-latency inference, tokens-per-second on transformers like Llama 70B depends heavily on quantisation strategy — FP8/INT8 unlock the compute ceiling, FP16 is bandwidth-bound.

The NVIDIA GeForce RTX 3080 page has the complete datasheet and side-by-side comparisons.

NVIDIA GeForce RTX 3080 is best for workloads where its 10 GB VRAM and Ampere tensor cores are well-matched: Gaming, inference.

If your workload needs significantly more memory (e.g., training frontier-scale models from scratch), NVIDIA GeForce RTX 3080 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 RTX 3080 is usually the sensible pick.

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