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 16GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 100% higher (576 GB/s vs 288 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Radeon PRO W7700 vs Radeon PRO W7800: In-Depth Breakdown
VRAM: Radeon PRO W7700 vs Radeon PRO W7800
The Radeon PRO W7800 carries 32GB of VRAM versus 16GB on the Radeon PRO W7700. 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 16GB 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 Radeon PRO W7800 delivers 576 GB/s versus 288 GB/s on the Radeon PRO W7700, a 100% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Radeon PRO W7800 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 34.6 TFLOPS for the Radeon PRO W7700 — a 31% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Radeon PRO W7800.
Which should you buy: Radeon PRO W7700 or Radeon PRO W7800?
The Radeon PRO W7800 is the stronger choice for large-model workloads where VRAM is the bottleneck. The Radeon PRO W7700 is more economical, and sufficient if your models fit within its 16GB.