Rent NVIDIA GeForce RTX 4060 in the Cloud

NVIDIA GeForce RTX 4060 packs 8 GB of GDDR6 with 272 GB/s of bandwidth, delivers 15.1 TFLOPS of FP16 compute, and operates at 115W. It's based on the Ada Lovelace architecture introduced in 2023.

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 4060 tends to sit in a balanced spot on all three — which is why it shows up repeatedly in cloud catalogues aimed at ML teams.

Review full specs and related comparisons on the NVIDIA GeForce RTX 4060 page.

How fast is NVIDIA GeForce RTX 4060? The raw numbers: 15.1 TFLOPS FP16, 7.6 TFLOPS FP32, 272 GB/s memory bandwidth. In mixed-precision AI jobs, that translates to sustained throughput comfortably above older generations.

For model training, expect wall-clock times that scale predictably from those TFLOPS figures at large batch sizes. For low-latency inference, real-world latency is dominated by memory bandwidth and by how much of your KV-cache fits on-chip — so the 272 GB/s and 8 GB capacity matter more than headline TFLOPS.

See the NVIDIA GeForce RTX 4060 page for the full spec sheet and comparisons to related GPUs.

NVIDIA GeForce RTX 4060 shines for Entry-level inference, gaming. The card's 8 GB of VRAM and Ada Lovelace-generation tensor cores make it a strong match for AI AI workloads that push batch sizes hard and benefit from modern mixed-precision support.

It's overkill for toy experiments and under-sized for the largest frontier models, but covers the vast middle: fine-tuning open-source transformers, training diffusion models, running batch real-time serving pipelines, and supporting interactive workflows that need more VRAM than a consumer card offers.

See the NVIDIA GeForce RTX 4060 page for the full spec sheet and comparisons to related GPUs.