GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX 6000 Ada Generation has more VRAM (48GB vs 20GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 243% higher (960 GB/s vs 280 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
RTX 4000 SFF Ada Generation vs RTX 6000 Ada Generation: In-Depth Breakdown
VRAM: RTX 4000 SFF Ada Generation vs RTX 6000 Ada Generation
The RTX 6000 Ada Generation carries 48GB of VRAM versus 20GB on the RTX 4000 SFF Ada Generation. 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 28GB advantage here means the RTX 6000 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 RTX 6000 Ada Generation delivers 960 GB/s versus 280 GB/s on the RTX 4000 SFF Ada Generation, a 243% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX 6000 Ada Generation 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 6000 Ada Generation delivers 91.1 TFLOPS against 19.2 TFLOPS for the RTX 4000 SFF Ada Generation — a 374% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX 6000 Ada Generation.
Which should you buy: RTX 4000 SFF Ada Generation or RTX 6000 Ada Generation?
The RTX 6000 Ada Generation is the stronger choice for large-model workloads where VRAM is the bottleneck. The RTX 4000 SFF Ada Generation is more economical, and sufficient if your models fit within its 20GB.