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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 16GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 94% higher (870 GB/s vs 448 GB/s), translating directly to faster inference throughput.

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

VS
NVIDIA
Quadro GV100
Price
Awaiting Data
VRAM
32 GB HBM2✓
Mem. Speed
870 GB/s✓
FP32 Compute
14.8 TFLOPS✓
Key Specs Advantage
+1500% Memory Bus (4096-bit vs 256-bit)
+94% Bandwidth (870 GB/s vs 448 GB/s)
+67% CUDA Cores (5,120 vs 3,072)
NVIDIA
Quadro RTX 5000
Price
₹129,744✓
VRAM
16 GB GDDR6
Mem. Speed
448 GB/s
FP32 Compute
11.2 TFLOPS
Key Specs Advantage

Comparable or lower specs

Skip to Spec Sheets ↓

Quadro GV100 vs Quadro RTX 5000: In-Depth Breakdown

VRAM: Quadro GV100 vs Quadro RTX 5000

The Quadro GV100 carries 32GB of VRAM versus 16GB on the Quadro RTX 5000. 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 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 448 GB/s on the Quadro RTX 5000, a 94% 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 11.2 TFLOPS for the Quadro RTX 5000 — a 32% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro GV100.

Which should you buy: Quadro GV100 or Quadro RTX 5000?

The Quadro GV100 is the stronger choice for large-model workloads where VRAM is the bottleneck. The Quadro RTX 5000 is more economical, and sufficient if your models fit within its 16GB.

Frequently Asked Questions

Can the Quadro GV100 or Quadro RTX 5000 run large language models?

Both can, but the Quadro GV100 (32GB) handles larger models without quantization. The Quadro RTX 5000 (16GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the Quadro GV100 or the Quadro RTX 5000?

The Quadro GV100 is faster for token generation — its 870 GB/s memory bandwidth vs 448 GB/s on the Quadro RTX 5000 is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The Quadro GV100 has the advantage at 14.8 TFLOPS vs 11.2 TFLOPS, making training runs proportionally faster than on the Quadro RTX 5000.

Technical Specifications Comparison

Architecture & Cores

SpecificationQuadro GV100Quadro RTX 5000
ArchitectureVoltaTuring
CUDA Cores (CUDA Cores / CUDA Cores)5,120✓3,072

Memory

SpecificationQuadro GV100Quadro RTX 5000
VRAM Capacity32 GB✓16 GB
Memory TypeHBM2GDDR6
Memory Bus4096-bit✓256-bit
Bandwidth870 GB/s✓448 GB/s

Connectivity & Power

SpecificationQuadro GV100Quadro RTX 5000
InterfacePCIe 3.0 x16PCIe 3.0 x16
TDP250 W230 W✓
ReleasedMar 2018Oct 2018

Workstation

SpecificationQuadro GV100Quadro RTX 5000
FP32 (TFLOPS)14.8 TFLOPS✓11.2 TFLOPS
ECCYesYes
NVLinkYes✓No
Form factordual-slotdual-slot
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