GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The Arc Pro A60 has more VRAM (24GB vs 20GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 67% higher (640 GB/s vs 384 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Arc Pro A60 vs RTX A4500: In-Depth Breakdown
VRAM: Arc Pro A60 vs RTX A4500
The Arc Pro A60 carries 24GB of VRAM versus 20GB on the RTX A4500. 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 Arc Pro A60 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 384 GB/s on the Arc Pro A60, a 67% 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 14.7 TFLOPS for the Arc Pro A60 — a 61% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX A4500.
Which should you buy: Arc Pro A60 or RTX A4500?
These cards suit different priorities. Choose the Arc Pro A60 if fitting larger models in VRAM is your constraint. Choose the RTX A4500 if your models already fit and you want faster inference throughput from its higher memory bandwidth.