GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX PRO 6000 Blackwell has more VRAM (96GB vs 24GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 367% higher (1792 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 PRO 6000 Blackwell: In-Depth Breakdown
VRAM: Arc Pro A60 vs RTX PRO 6000 Blackwell
The RTX PRO 6000 Blackwell carries 96GB of VRAM versus 24GB on the Arc Pro A60. 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 72GB advantage here means the RTX PRO 6000 Blackwell 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 PRO 6000 Blackwell delivers 1792 GB/s versus 384 GB/s on the Arc Pro A60, a 367% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX PRO 6000 Blackwell 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 PRO 6000 Blackwell delivers 125 TFLOPS against 14.7 TFLOPS for the Arc Pro A60 — a 750% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX PRO 6000 Blackwell.
Which should you buy: Arc Pro A60 or RTX PRO 6000 Blackwell?
The RTX PRO 6000 Blackwell is the stronger choice for large-model workloads where VRAM is the bottleneck. The Arc Pro A60 is more economical, and sufficient if your models fit within its 24GB.