GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The Quadro GV100 has more VRAM (32GB vs 6GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 159% higher (870 GB/s vs 336 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Quadro GV100 vs Quadro RTX 3000: In-Depth Breakdown
VRAM: Quadro GV100 vs Quadro RTX 3000
The Quadro GV100 carries 32GB of VRAM versus 6GB on the Quadro RTX 3000. 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 26GB advantage here means the Quadro GV100 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 Quadro GV100 delivers 870 GB/s versus 336 GB/s on the Quadro RTX 3000, a 159% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Quadro GV100 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 Quadro GV100 delivers 14.8 TFLOPS against 5.3 TFLOPS for the Quadro RTX 3000 — a 179% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro GV100.
Which should you buy: Quadro GV100 or Quadro RTX 3000?
The Quadro GV100 is the stronger choice for large-model workloads where VRAM is the bottleneck. The Quadro RTX 3000 is more economical, and sufficient if your models fit within its 6GB.