Rent NVIDIA GeForce RTX 5070 Ti in the Cloud

Technical profile of NVIDIA GeForce RTX 5070 Ti: Blackwell design, 16 GB GDDR7 VRAM, 896 GB/s memory bandwidth, 44/22 TFLOPS FP16/FP32, 300W TDP, released 2025.

Each number maps to a practical concern. VRAM governs model size. Bandwidth throttles decoding speed on autoregressive models. FP16 TFLOPS dominates large-batch pre-model training. FP32 TFLOPS matters less for deep learning but is important for scientific simulation and HPC. TDP influences cooling, rack density, and ultimately cloud pricing.

The NVIDIA GeForce RTX 5070 Ti page has the complete datasheet and side-by-side comparisons.

NVIDIA GeForce RTX 5070 Ti hits 44 TFLOPS of FP16 compute with 896 GB/s of memory bandwidth and 16 GB of VRAM. FP32 peaks at 22 TFLOPS.

Those figures place NVIDIA GeForce RTX 5070 Ti in a useful performance band for generative AI work: strong enough to pre-training mid-to-large models in reasonable time, with enough bandwidth to keep real-time serving latency low. Actual tokens-per-second or images-per-second varies 2x depending on framework, quantisation, and model size — always benchmark with the exact stack you plan to ship.

Full specs, benchmarks, and comparisons are on the NVIDIA GeForce RTX 5070 Ti page.

NVIDIA GeForce RTX 5070 Ti is best for workloads where its 16 GB VRAM and Blackwell 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 5070 Ti 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 5070 Ti is usually the sensible pick.

Check the NVIDIA GeForce RTX 5070 Ti page for complete specifications and related GPU matchups.