Quadro RTX 6000

Architecture · 24GB · PCIe

VRAM
24GB
FP16
-
TDP
-
Hardware Price
-
Cloud from
$0.500/hr
1 providers
Cheapest at Lambda Labs →

Quick Insights

Performance/Dollar
N/A
FP16 performance per $1000
VRAM/Dollar
N/A
VRAM per $1000
vs null Average
N/A
FP16 TFLOPS comparison
Cloud Availability
1 providers
from $0.500/hr

Specifications

VRAM 24GB
Memory Bandwidth -
FP16 TFLOPS -
Tensor TFLOPS -
FP32 TFLOPS -
TDP -
Form Factor -
Architecture -
NVLink No
Release Date -

Cloud GPU Pricing

Rent Quadro RTX 6000 from 1 cloud providers. Prices shown per GPU per hour.

Provider Type Instance GPUs On-Demand Per GPU Spot Availability
Lambda Labs gpu-cloud lambda-quadro-rtx-6000 1x $0.500/hr $0.500/hr Cheapest - -

Quadro RTX 6000 vs Alternatives

Compare Quadro RTX 6000 with similar GPUs from other brands.

GPU VRAM FP16 TFLOPS Bandwidth Hardware Price Cloud Price
Quadro RTX 6000 Current 24GB - - - - -
AMD Radeon RX 7900 XTX AMD 24GB (+0%) 122.0 960 GB/s - - Compare
AMD Radeon RX 7900 XT AMD 20GB (-17%) 104.0 800 GB/s - - Compare
AMD Instinct MI100 AMD 32GB (+33%) 184.6 1.2 TB/s - - Compare

Best Use Cases

No specific use case recommendations for Quadro RTX 6000 yet.

Browse All Use Cases →

Compare Quadro RTX 6000

Other NVIDIA GPUs

Frequently Asked Questions about Quadro RTX 6000

Pricing for Quadro RTX 6000 varies. Check our cloud pricing section for rental options starting at $0.500/hr.

Yes, the Quadro RTX 6000 with 24GB VRAM is suitable for many AI/ML workloads. For large language models, you may need multiple GPUs or consider higher-VRAM options like A100 or H100.

Consider buying for long-term, heavy usage (>4 hrs/day). Rent from cloud providers for short-term projects, experimentation, or when you need to scale quickly.

With 24GB VRAM and - FP16 TFLOPS, the Quadro RTX 6000 can run: Large language models (7B-13B), Stable Diffusion XL, video AI, and professional 3D rendering.

The Quadro RTX 6000 offers 24GB VRAM and - FP16 performance at its price point. Compare with similar GPUs using our comparison tool above. Key factors: VRAM for model size, TFLOPS for speed, and price for budget.