GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The Radeon PRO W7800 has more VRAM (32GB vs 20GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 11% higher (640 GB/s vs 576 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Radeon PRO W7800 vs RTX A4500: In-Depth Breakdown
VRAM: Radeon PRO W7800 vs RTX A4500
The Radeon PRO W7800 carries 32GB of VRAM versus 20GB on the RTX A4500. 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 Radeon PRO W7800 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 A4500 delivers 640 GB/s versus 576 GB/s on the Radeon PRO W7800, a 11% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A4500 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 W7800 delivers 45.2 TFLOPS against 23.7 TFLOPS for the RTX A4500 — a 91% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Radeon PRO W7800.
Which should you buy: Radeon PRO W7800 or RTX A4500?
These cards suit different priorities. Choose the Radeon PRO W7800 if fitting larger models in VRAM is your constraint. Choose the RTX A4500 if your models already fit and you want faster inference throughput from its higher memory bandwidth.