Quadro GV100 vs Quadro RTX 8000
The Quadro RTX 8000 has more VRAM (48GB vs 32GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 39% higher (870 GB/s vs 624 GB/s), translating directly to faster inference throughput. The Quadro GV100 is $284 EUR cheaper than the Quadro RTX 8000.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Quadro GV100 vs Quadro RTX 8000: In-Depth Breakdown
VRAM: Quadro GV100 vs Quadro RTX 8000
The Quadro RTX 8000 carries 48GB of VRAM versus 32GB on the Quadro GV100. 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 16GB 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 GV100 delivers 870 GB/s versus 624 GB/s on the Quadro RTX 8000, a 39% 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 RTX 8000 delivers 14.9 TFLOPS against 14.8 TFLOPS for the Quadro GV100 — a 1% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro RTX 8000.
Price & Value
The Quadro GV100 lists from $3,500 EUR, $284 EUR less than the Quadro RTX 8000 at $3,784 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 8000?
These cards suit different priorities. Choose the Quadro RTX 8000 if fitting larger models in VRAM is your constraint. Choose the Quadro GV100 if your models already fit and you want faster inference throughput from its higher memory bandwidth.
Frequently Asked Questions
Can the Quadro GV100 or Quadro RTX 8000 run large language models?
Which is faster for LLM inference, the Quadro GV100 or the Quadro RTX 8000?
Which is better for AI training?
Which should you buy: Quadro GV100 or Quadro RTX 8000?
Technical Specifications Comparison
Architecture & Cores
| Specification | Quadro GV100 | Quadro RTX 8000 |
|---|---|---|
| Architecture | Volta | Turing |
| CUDA Cores (CUDA Cores / CUDA Cores) | 5,120✓ | 4,608 |
Memory
| Specification | Quadro GV100 | Quadro RTX 8000 |
|---|---|---|
| VRAM Capacity | 32 GB | 48 GB✓ |
| Memory Type | HBM2 | GDDR6 |
| Memory Bus | 4096-bit✓ | 384-bit |
| Bandwidth | 870 GB/s✓ | 624 GB/s |
Connectivity & Power
| Specification | Quadro GV100 | Quadro RTX 8000 |
|---|---|---|
| Interface | PCIe 3.0 x16 | PCIe 3.0 x16 |
| TDP | 250 W✓ | 295 W |
| Released | Mar 2018 | Oct 2018 |
Workstation
| Specification | Quadro GV100 | Quadro RTX 8000 |
|---|---|---|
| FP32 (TFLOPS) | 14.8 TFLOPS | 14.9 TFLOPS✓ |
| ECC | Yes | Yes |
| NVLink | Yes | Yes |
| Form factor | dual-slot | dual-slot |