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 12GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 33% higher (384 GB/s vs 288 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Arc Pro A60 vs RTX A2000: In-Depth Breakdown
VRAM: Arc Pro A60 vs RTX A2000
The Arc Pro A60 carries 24GB of VRAM versus 12GB on the RTX A2000. 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 12GB 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 288 GB/s on the RTX A2000, a 33% 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 Arc Pro A60 delivers 14.7 TFLOPS against 8 TFLOPS for the RTX A2000 — a 84% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Arc Pro A60.
Which should you buy: Arc Pro A60 or RTX A2000?
The Arc Pro A60 is the stronger choice for large-model workloads where VRAM is the bottleneck. The RTX A2000 is more economical, and sufficient if your models fit within its 12GB.