Rent NVIDIA GeForce RTX 3070 in the Cloud

Released in 2020, NVIDIA GeForce RTX 3070 is an Ampere-class accelerator with 8 GB of GDDR6, 448 GB/s of memory bandwidth, and 20.3 FP16 TFLOPS of compute. FP32 peaks at 10.2 TFLOPS and the card draws up to 220W.

In practical terms: enough VRAM to load models into the ~8B-parameter range in FP16 (larger with quantisation), enough bandwidth to avoid memory-starving attention layers, and enough compute to train transformers at batch sizes that saturate modern optimisers.

See the NVIDIA GeForce RTX 3070 page for the full spec sheet and comparisons to related GPUs.

NVIDIA GeForce RTX 3070 hits 20.3 TFLOPS of FP16 compute with 448 GB/s of memory bandwidth and 8 GB of VRAM. FP32 peaks at 10.2 TFLOPS.

Those figures place NVIDIA GeForce RTX 3070 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.

The NVIDIA GeForce RTX 3070 page has the complete datasheet and side-by-side comparisons.

Use cases where NVIDIA GeForce RTX 3070 performs well: Budget inference, gaming. The card's 8 GB of VRAM, Ampere tensor cores, and consumer positioning suit it for AI teams who need cloud-grade hardware without the premium of the newest accelerators.

A good rule of thumb: if your model fits in 8 GB of VRAM at your target precision, and your workload is bandwidth- or compute-bound rather than memory-bound, NVIDIA GeForce RTX 3070 is likely a better economic choice than upgrading to a higher tier.

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