Rent NVIDIA GeForce RTX 5080 in the Cloud

NVIDIA GeForce RTX 5080 is a 2025-generation Blackwell card with 16 GB of GDDR7 memory and 960 GB/s bandwidth. Compute peaks at 56 FP16 TFLOPS and 28 FP32 TFLOPS; TDP sits at 360W.

The VRAM/bandwidth pairing is the defining feature for machine learning work — it determines what model sizes are accessible and how hard the card can be pushed during production inference. Power draw and cooling requirements mean most NVIDIA GeForce RTX 5080 deployments live in data centres rather than workstations, which is why most NVIDIA GeForce RTX 5080 access in practice comes via the cloud.

See the NVIDIA GeForce RTX 5080 page for the full spec sheet and comparisons to related GPUs.

NVIDIA GeForce RTX 5080 pushes 56 TFLOPS of FP16, 28 TFLOPS of FP32, and feeds them from 16 GB of VRAM at 960 GB/s.

Benchmarks: LLM training with mixed precision sees near-peak FLOPS utilisation at batch sizes that fit in VRAM; LLM inference is typically within 5-15% of the theoretical bandwidth-bound ceiling on autoregressive decoding; diffusion models show the biggest jump over older accelerators, where faster attention kernels stack with the raw compute gains.

The NVIDIA GeForce RTX 5080 page has the complete datasheet and side-by-side comparisons.

NVIDIA GeForce RTX 5080 is best for workloads where its 16 GB VRAM and Blackwell tensor cores are well-matched: Gaming, inference, fine-tuning.

If your workload needs significantly more memory (e.g., training frontier-scale models from scratch), NVIDIA GeForce RTX 5080 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 5080 is usually the sensible pick.

See the NVIDIA GeForce RTX 5080 page for the full spec sheet and comparisons to related GPUs.