GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX A4500 has more VRAM (20GB vs 6GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 233% higher (640 GB/s vs 192 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Arc Pro A40 vs RTX A4500: In-Depth Breakdown
VRAM: Arc Pro A40 vs RTX A4500
The RTX A4500 carries 20GB of VRAM versus 6GB on the Arc Pro A40. 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 14GB advantage here means the RTX A4500 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 A4500 delivers 640 GB/s versus 192 GB/s on the Arc Pro A40, a 233% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A4500 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 A4500 delivers 23.7 TFLOPS against 3.7 TFLOPS for the Arc Pro A40 — a 541% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX A4500.
Which should you buy: Arc Pro A40 or RTX A4500?
The RTX A4500 is the stronger choice for large-model workloads where VRAM is the bottleneck. The Arc Pro A40 is more economical, and sufficient if your models fit within its 6GB.