GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX A5000 has more VRAM (24GB vs 20GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 113% higher (768 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 A5000: In-Depth Breakdown
VRAM: RTX 4000 Ada Generation vs RTX A5000
The RTX A5000 carries 24GB of VRAM versus 20GB on the RTX 4000 Ada Generation. 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 A5000 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 A5000 delivers 768 GB/s versus 360 GB/s on the RTX 4000 Ada Generation, a 113% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A5000 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 A5000 delivers 27.8 TFLOPS against 26.7 TFLOPS for the RTX 4000 Ada Generation — a 4% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX A5000.
Which should you buy: RTX 4000 Ada Generation or RTX A5000?
The RTX A5000 is the stronger choice for large-model workloads where VRAM is the bottleneck. The RTX 4000 Ada Generation is more economical, and sufficient if your models fit within its 20GB.