GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX 4000 SFF Ada Generation has more VRAM (20GB vs 16GB), making it better suited for large models and memory-intensive workloads.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Radeon PRO W7700 vs RTX 4000 SFF Ada Generation: In-Depth Breakdown
VRAM: Radeon PRO W7700 vs RTX 4000 SFF Ada Generation
The RTX 4000 SFF Ada Generation carries 20GB 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 4GB advantage here means the RTX 4000 SFF Ada Generation 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 W7700 delivers 288 GB/s versus 280 GB/s on the RTX 4000 SFF Ada Generation, a 3% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Radeon PRO W7700 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 W7700 delivers 34.6 TFLOPS against 19.2 TFLOPS for the RTX 4000 SFF Ada Generation — a 80% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Radeon PRO W7700.
Which should you buy: Radeon PRO W7700 or RTX 4000 SFF Ada Generation?
These cards suit different priorities. Choose the RTX 4000 SFF Ada Generation if fitting larger models in VRAM is your constraint. Choose the Radeon PRO W7700 if your models already fit and you want faster inference throughput from its higher memory bandwidth.