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 8GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 109% higher (870 GB/s vs 416 GB/s), translating directly to faster inference throughput. The Quadro RTX 4000 is $11,174 EUR cheaper than the Quadro GV100.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Quadro GV100 vs Quadro RTX 4000: In-Depth Breakdown
VRAM: Quadro GV100 vs Quadro RTX 4000
The Quadro GV100 carries 32GB of VRAM versus 8GB on the Quadro RTX 4000. 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 24GB 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 416 GB/s on the Quadro RTX 4000, a 109% 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 7.1 TFLOPS for the Quadro RTX 4000 — a 108% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro GV100.
Price & Value
The Quadro RTX 4000 lists from $426 EUR, $11,174 EUR less than the Quadro GV100 at $11,600 EUR. For budget-constrained teams, the savings may outweigh the spec gap — especially if the smaller card covers your typical workload.
Which should you buy: Quadro GV100 or Quadro RTX 4000?
Choose the Quadro GV100 for maximum capacity — it leads on VRAM, bandwidth, and compute, making it the better fit for large models and training jobs. The Quadro RTX 4000 is the more budget-friendly option ($11,174 EUR less) — a solid choice if your models fit within its 8GB and inference volume is moderate.