Rent NVIDIA RTX A4000 in the Cloud

Technical profile of NVIDIA RTX A4000: Ampere design, 16 GB GDDR6 VRAM, 448 GB/s memory bandwidth, 19.2/16 TFLOPS FP16/FP32, 140W TDP, released 2021.

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 RTX A4000 page has the complete datasheet and side-by-side comparisons.

NVIDIA RTX A4000 is a Ampere card offering 19.2 FP16 TFLOPS and 16 FP32 TFLOPS alongside 448 GB/s of memory bandwidth. That's enough compute to handle modern model training and real-time serving workloads at serious scale.

Benchmarks show NVIDIA RTX A4000 performs particularly well on transformer-style models where tensor cores are saturated by large MatMuls. Diffusion models, speech, and vision workloads also see strong speedups versus older generations. For latency-sensitive production real-time serving, NVIDIA RTX A4000 usually hits target token-per-second rates on large language models well above the 30-50 tok/s threshold most products aim for.

The NVIDIA RTX A4000 page has the complete datasheet and side-by-side comparisons.

NVIDIA RTX A4000 is best for workloads where its 16 GB VRAM and Ampere tensor cores are well-matched: Entry workstation, rendering.

If your workload needs significantly more memory (e.g., training frontier-scale models from scratch), NVIDIA RTX A4000 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 RTX A4000 is usually the sensible pick.

See the NVIDIA RTX A4000 page for the full spec sheet and comparisons to related GPUs.