GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The Quadro RTX 8000 has more VRAM (48GB vs 6GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 86% higher (624 GB/s vs 336 GB/s), translating directly to faster inference throughput.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Quadro RTX 3000 vs Quadro RTX 8000: In-Depth Breakdown
VRAM: Quadro RTX 3000 vs Quadro RTX 8000
The Quadro RTX 8000 carries 48GB 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 42GB advantage here means the Quadro RTX 8000 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 RTX 8000 delivers 624 GB/s versus 336 GB/s on the Quadro RTX 3000, a 86% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Quadro RTX 8000 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 RTX 8000 delivers 14.9 TFLOPS against 5.3 TFLOPS for the Quadro RTX 3000 — a 181% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro RTX 8000.
Which should you buy: Quadro RTX 3000 or Quadro RTX 8000?
The Quadro RTX 8000 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.