GPU NVIDIA

RTX 4000 Ada SFF

Architecture · 20GB · PCIe

VRAM
20GB
FP16
-
TDP
-
Hardware Price
-
Cloud from
$0.440/hr
1 providers
Cheapest at RunPod →

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 $0.440/hr

Specifications

VRAM 20GB
Memory Bandwidth -
FP16 TFLOPS -
Tensor TFLOPS -
FP32 TFLOPS -
TDP -
Form Factor -
Architecture -
NVLink No
Release Date -

Cloud GPU Pricing

Rent RTX 4000 Ada SFF from 1 cloud providers. Prices shown per GPU per hour.

Provider Type Instance GPUs On-Demand Per GPU Spot Availability
RunPod gpu-cloud NVIDIA RTX 4000 SFF Ada Generation 1x $0.440/hr $0.440/hr Cheapest $0.090/hr (-80%) -
Best Spot Deal: RunPod offers spot pricing at $0.090/hr (80% off on-demand).

RTX 4000 Ada SFF vs Alternatives

Compare RTX 4000 Ada SFF with similar GPUs from other brands.

GPU VRAM FP16 TFLOPS Bandwidth Hardware Price Cloud Price
RTX 4000 Ada SFF Current 20GB - - - - -
AMD Radeon RX 7900 XT AMD 20GB (+0%) 104.0 800 GB/s - - Compare
AMD Radeon RX 7900 XTX AMD 24GB (+20%) 122.0 960 GB/s - - Compare
AMD Instinct MI100 AMD 32GB (+60%) 184.6 1.2 TB/s - - Compare

Best Use Cases

No specific use case recommendations for RTX 4000 Ada SFF yet.

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Frequently Asked Questions about RTX 4000 Ada SFF

Pricing for RTX 4000 Ada SFF varies. Check our cloud pricing section for rental options starting at $0.440/hr.

Yes, the RTX 4000 Ada SFF with 20GB 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 20GB VRAM and - FP16 TFLOPS, the RTX 4000 Ada SFF can run: Stable Diffusion, smaller LLMs (7B quantized), deep learning training, and gaming at high settings.

The RTX 4000 Ada SFF offers 20GB 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.