Rent NVIDIA GeForce RTX 3070 Ti in the Cloud

Short version of the NVIDIA GeForce RTX 3070 Ti spec sheet: 8 GB GDDR6X, 608 GB/s, 21.7 FP16 TFLOPS, 10.8 FP32 TFLOPS, Ampere (2021), 290W.

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.

Check the NVIDIA GeForce RTX 3070 Ti page for complete specifications and related GPU matchups.

Raw compute on NVIDIA GeForce RTX 3070 Ti peaks at 21.7 FP16 TFLOPS and 10.8 FP32 TFLOPS, with 608 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.

Review full specs and related comparisons on the NVIDIA GeForce RTX 3070 Ti page.

NVIDIA GeForce RTX 3070 Ti is best for workloads where its 8 GB VRAM and Ampere tensor cores are well-matched: Budget inference, gaming.

If your workload needs significantly more memory (e.g., training frontier-scale models from scratch), NVIDIA GeForce RTX 3070 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 3070 Ti is usually the sensible pick.

Check the NVIDIA GeForce RTX 3070 Ti page for complete specifications and related GPU matchups.