GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX A6000 has more VRAM (48GB vs 12GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 167% higher (768 GB/s vs 288 GB/s), translating directly to faster inference throughput. The RTX A6000 is $150 USD cheaper than the RTX A2000.
Maximum Capacity Reached. Remove a model to add another. (2/2)
RTX A2000 vs RTX A6000: In-Depth Breakdown
VRAM: RTX A2000 vs RTX A6000
The RTX A6000 carries 48GB 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 36GB advantage here means the RTX A6000 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 A6000 delivers 768 GB/s versus 288 GB/s on the RTX A2000, a 167% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A6000 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 A6000 delivers 38.7 TFLOPS against 8 TFLOPS for the RTX A2000 — a 384% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX A6000.
Price & Value
The RTX A6000 lists from $200 USD, $150 USD less than the RTX A2000 at $350 USD. For budget-constrained teams, the savings may outweigh the spec gap — especially if the smaller card covers your typical workload.
Which should you buy: RTX A2000 or RTX A6000?
The RTX A6000 is the stronger choice for large-model workloads where VRAM is the bottleneck. The RTX A2000 is more economical at $150 USD less, and sufficient if your models fit within its 12GB.