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.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Arc Pro A60 vs RTX 4000 Ada Generation: In-Depth Breakdown
VRAM: Arc Pro A60 vs RTX 4000 Ada Generation
The Arc Pro A60 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 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 Arc Pro A60 delivers 384 GB/s versus 360 GB/s on the RTX 4000 Ada Generation, a 7% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Arc Pro A60 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 14.7 TFLOPS for the Arc Pro A60 — a 82% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX 4000 Ada Generation.
Which should you buy: Arc Pro A60 or RTX 4000 Ada Generation?
The Arc Pro A60 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.