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. Memory bandwidth runs 39% ahead (870 GB/s versus 624 GB/s), which feeds straight through into quicker inference. The Quadro GV100 is $720 USD 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
The rate data moves to the compute units — memory bandwidth — is what caps autoregressive inference speed (token generation in LLMs). Here the Quadro GV100 reaches 870 GB/s against 624 GB/s for the Quadro RTX 8000, a 39% advantage. Once a model is resident in VRAM, token throughput tracks this figure closely, so the Quadro GV100 generates tokens proportionally faster on 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
Pricing starts at $1,750 USD for the Quadro GV100, a $720 USD saving over the Quadro RTX 8000's $2,470 USD. Teams working within a tighter budget may find that saving worth more than the spec gap, particularly if the smaller card already covers their 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 |