GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX 4000 Ada Generation has more VRAM (20GB vs 16GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 24% higher (448 GB/s vs 360 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
RTX 4000 Ada Generation vs RTX A4000: In-Depth Breakdown
VRAM: RTX 4000 Ada Generation vs RTX A4000
The RTX 4000 Ada Generation carries 20GB of VRAM versus 16GB on the RTX A4000. VRAM capacity is the primary constraint for running AI models without quantization — a 70B-parameter model in FP16 requires roughly 140GB, and even smaller models benefit from extra headroom. The 4GB advantage here means the RTX 4000 Ada Generation can run larger models natively and handle bigger batch sizes in production.
Inference Speed: Memory Bandwidth
Memory bandwidth determines how quickly data is fed to the compute units — it's the main bottleneck for autoregressive inference (token generation in LLMs). The RTX A4000 delivers 448 GB/s versus 360 GB/s on the RTX 4000 Ada Generation, a 24% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A4000 will produce tokens proportionally faster in bandwidth-bound workloads.
AI Training & Compute
For model training, scientific simulation, and rendering, FP32 throughput is the key metric. The RTX 4000 Ada Generation delivers 26.7 TFLOPS against 19.2 TFLOPS for the RTX A4000 — a 39% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX 4000 Ada Generation.
Which should you buy: RTX 4000 Ada Generation or RTX A4000?
These cards suit different priorities. Choose the RTX 4000 Ada Generation if fitting larger models in VRAM is your constraint. Choose the RTX A4000 if your models already fit and you want faster inference throughput from its higher memory bandwidth.