GPU Comparison

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

Workstation Verdict

The Quadro RTX 6000 has more VRAM (24GB vs 16GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 39% higher (624 GB/s vs 448 GB/s), translating directly to faster inference throughput. The Quadro RTX 5000 is $1,008 EUR cheaper than the Quadro RTX 6000.

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

VS
NVIDIA
Quadro RTX 5000
Price
€742
VRAM
16 GB GDDR6
Mem. Speed
448 GB/s
FP32 Compute
11.2 TFLOPS
Key Specs Advantage

Comparable or lower specs

Price
€1,750
VRAM
24 GB GDDR6
Mem. Speed
624 GB/s
FP32 Compute
16.3 TFLOPS
Key Specs Advantage
+50% CUDA Cores (4,608 vs 3,072)
+50% Memory Bus (384-bit vs 256-bit)
+46% FP32 (TFLOPS) (16.3 TFLOPS vs 11.2 TFLOPS)

Quadro RTX 5000 vs Quadro RTX 6000: In-Depth Breakdown

VRAM: Quadro RTX 5000 vs Quadro RTX 6000

The Quadro RTX 6000 carries 24GB 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 8GB advantage here means the Quadro RTX 6000 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 6000 delivers 624 GB/s versus 448 GB/s on the Quadro RTX 5000, a 39% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Quadro RTX 6000 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 11.2 TFLOPS for the Quadro RTX 5000 — a 46% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro RTX 6000.

Price & Value

The Quadro RTX 5000 lists from $742 EUR, $1,008 EUR less than the Quadro RTX 6000 at $1,750 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 RTX 5000 or Quadro RTX 6000?

Choose the Quadro RTX 6000 for maximum capacity — it leads on VRAM, bandwidth, and compute, making it the better fit for large models and training jobs. The Quadro RTX 5000 is the more budget-friendly option ($1,008 EUR less) — a solid choice if your models fit within its 16GB and inference volume is moderate.

Frequently Asked Questions

Can the Quadro RTX 5000 or Quadro RTX 6000 run large language models?

Both can, but the Quadro RTX 6000 (24GB) 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 RTX 5000 or the Quadro RTX 6000?

The Quadro RTX 6000 is faster for token generation — its 624 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 RTX 6000 has the advantage at 16.3 TFLOPS vs 11.2 TFLOPS, making training runs proportionally faster than on the Quadro RTX 5000.

Technical Specifications Comparison

Architecture & Cores

SpecificationQuadro RTX 5000Quadro RTX 6000
ArchitectureTuringTuring
CUDA Cores (CUDA Cores / CUDA Cores)3,0724,608

Memory

SpecificationQuadro RTX 5000Quadro RTX 6000
VRAM Capacity16 GB24 GB
Memory TypeGDDR6GDDR6
Memory Bus256-bit384-bit
Bandwidth448 GB/s624 GB/s

Connectivity & Power

SpecificationQuadro RTX 5000Quadro RTX 6000
InterfacePCIe 3.0 x16PCIe 3.0 x16
TDP230 W295 W
ReleasedOct 2018Oct 2018

Workstation

SpecificationQuadro RTX 5000Quadro RTX 6000
FP32 (TFLOPS)11.2 TFLOPS16.3 TFLOPS
ECCYesYes
NVLinkNoYes
Form factordual-slotdual-slot