GPU Comparison

Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.

Workstation Verdict

The Quadro GV100 has more VRAM (32GB vs 24GB), 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 RTX 6000 is $1,414 USD cheaper than the Quadro GV100.

Maximum Capacity Reached. Remove a model to add another. (2/2)

VS
Price
$2,809 USD
VRAM
32 GB HBM2
Mem. Speed
870 GB/s
FP32 Compute
14.8 TFLOPS
Key Specs Advantage
+967% Memory Bus (4096-bit vs 384-bit)
+39% Bandwidth (870 GB/s vs 624 GB/s)
+11% CUDA Cores (5,120 vs 4,608)
NVIDIA
Quadro RTX 6000
Price
$1,395 USD
VRAM
24 GB GDDR6
Mem. Speed
624 GB/s
FP32 Compute
16.3 TFLOPS
Key Specs Advantage
+10% FP32 (TFLOPS) (16.3 TFLOPS vs 14.8 TFLOPS)

Quadro GV100 vs Quadro RTX 6000: In-Depth Breakdown

VRAM: Quadro GV100 vs Quadro RTX 6000

The Quadro GV100 carries 32GB of VRAM versus 24GB on the Quadro RTX 6000. 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 8GB 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 624 GB/s on the Quadro RTX 6000, 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 6000 delivers 16.3 TFLOPS against 14.8 TFLOPS for the Quadro GV100 — a 10% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro RTX 6000.

Price & Value

The Quadro RTX 6000 lists from $1,395 USD, $1,414 USD less than the Quadro GV100 at $2,809 USD. 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 6000?

The Quadro GV100 is the stronger choice for large-model workloads where VRAM is the bottleneck. The Quadro RTX 6000 is more economical at $1,414 USD less, and sufficient if your models fit within its 24GB.

Frequently Asked Questions

Can the Quadro GV100 or Quadro RTX 6000 run large language models?

Both can, but the Quadro GV100 (32GB) handles larger models without quantization. The Quadro RTX 6000 (24GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the Quadro GV100 or the Quadro RTX 6000?

The Quadro GV100 is faster for token generation — its 870 GB/s memory bandwidth vs 624 GB/s on the Quadro RTX 6000 is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The Quadro RTX 6000 has the advantage at 16.3 TFLOPS vs 14.8 TFLOPS, making training runs proportionally faster than on the Quadro GV100.

Technical Specifications Comparison

Architecture & Cores

SpecificationQuadro GV100Quadro RTX 6000
ArchitectureVoltaTuring
CUDA Cores (CUDA Cores / CUDA Cores)5,1204,608

Memory

SpecificationQuadro GV100Quadro RTX 6000
VRAM Capacity32 GB24 GB
Memory TypeHBM2GDDR6
Memory Bus4096-bit384-bit
Bandwidth870 GB/s624 GB/s

Connectivity & Power

SpecificationQuadro GV100Quadro RTX 6000
InterfacePCIe 3.0 x16PCIe 3.0 x16
TDP250 W295 W
ReleasedMar 2018Oct 2018

Workstation

SpecificationQuadro GV100Quadro RTX 6000
FP32 (TFLOPS)14.8 TFLOPS16.3 TFLOPS
ECCYesYes
NVLinkYesYes
Form factordual-slotdual-slot