GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The Radeon PRO W7900 has more VRAM (48GB vs 12GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 200% higher (864 GB/s vs 288 GB/s), translating directly to faster inference throughput. The RTX A2000 is $9,662 CAD cheaper than the Radeon PRO W7900.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Radeon PRO W7900 vs RTX A2000: In-Depth Breakdown
VRAM: Radeon PRO W7900 vs RTX A2000
The Radeon PRO W7900 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 Radeon PRO W7900 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 Radeon PRO W7900 delivers 864 GB/s versus 288 GB/s on the RTX A2000, a 200% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Radeon PRO W7900 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 Radeon PRO W7900 delivers 61.3 TFLOPS against 8 TFLOPS for the RTX A2000 — a 666% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Radeon PRO W7900.
Price & Value
The RTX A2000 lists from $14 CAD, $9,662 CAD less than the Radeon PRO W7900 at $9,676 CAD. For budget-constrained teams, the savings may outweigh the spec gap — especially if the smaller card covers your typical workload.
Which should you buy: Radeon PRO W7900 or RTX A2000?
Choose the Radeon PRO W7900 for maximum capacity — it leads on VRAM, bandwidth, and compute, making it the better fit for large models and training jobs. The RTX A2000 is the more budget-friendly option ($9,662 CAD less) — a solid choice if your models fit within its 12GB and inference volume is moderate.