Specifications Compared
| Spec | RTX-4000-ADA | RTX-4080 |
|---|---|---|
| TDP | 130W | 320W |
| VRAM | 20 GB | 16 GB |
| CUDA Cores | 6,144 | 9,728 |
| Memory Type | GDDR6 | GDDR6X |
| Architecture | Ada Lovelace | Ada Lovelace |
| Form Factors | PCIe | PCIe |
| Interconnect | ||
| Tensor Cores | 192 | 304 |
| FP16 Performance | 26.7 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 26.7 TFLOPS | 48.7 TFLOPS |
| INT8 Performance | 427 TOPS | 780 TOPS |
| Memory Bandwidth | 360 GB/s | 717 GB/s |
Performance Analysis
Compute performance differs markedly between these GPUs: the RTX 4080 SUPER achieves 48.7 TFLOPS in FP16 and FP32, 82 percent higher than the RTX 4000 Ada Generation's 26.7 TFLOPS. This gap accelerates deep learning training, where FP16 tensor operations dominate, and FP32 precision tasks in simulations. Inference workloads similarly benefit from the RTX 4080 SUPER's higher throughput, reducing latency for real-time serving. Memory bandwidth tells another story: 717 GB/s on the RTX 4080 SUPER versus 360 GB/s on the RTX 4000 Ada Generation enables larger batch sizes in training, minimizing data bottlenecks during gradient computations. The RTX 4000 Ada Generation counters with 20 GB GDDR6 VRAM against 16 GB GDDR6X, supporting bigger models or sequences without swapping to system RAM. Power draw impacts sustained cloud runs: 130W TDP for RTX 4000 Ada Generation yields lower costs over time compared to 320W on RTX 4080 SUPER.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4000 Ada Generation
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 8 vCPU 50GB RAM | 🌍global | $0.26/GPU/hr | |||
 Vast.ai | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 64 vCPU 42GB RAM 505GB Storage | Hungary | $0.40/GPU/hr | Available | ||
RunPod | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 8 vCPU 50GB RAM | 🌍global | $0.44/GPU/hr | |||
RunPod | NVIDIA RTX 4000 Ada Generation 20GB VRAM | 20GB | 0 vCPU 0GB RAM | 🌍global | $0.57/GPU/hr |
RTX 4080 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
RunPod | NVIDIA GeForce RTX 4080 SUPER 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA GeForce RTX 4080 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the RTX 4000 Ada Generation
The RTX 4000 Ada Generation excels in memory-bound inference for large language models requiring over 16 GB VRAM. Its 20 GB capacity handles extended contexts without quantization, ideal for deployments on tight budgets at $0.09/hr starting price. Lower 130W TDP suits prolonged tasks where energy efficiency matters more than peak speed.
When to Choose the RTX 4080 SUPER
Choose the RTX 4080 SUPER for compute-intensive training or fine-tuning, leveraging 48.7 TFLOPS and 717 GB/s bandwidth for faster iterations on datasets. High-throughput Stable Diffusion generation benefits from doubled performance over the RTX 4000 Ada Generation's 26.7 TFLOPS. Despite higher $0.17/hr entry cost, it delivers value in time-sensitive workflows.
Use Cases
RTX 4080 SUPER's 48.7 TFLOPS FP16 and 717 GB/s bandwidth support larger batches and quicker epochs than RTX 4000 Ada Generation's 26.7 TFLOPS and 360 GB/s.
RTX 4000 Ada Generation's 20 GB VRAM accommodates larger models without offloading, suitable for cost-effective serving at $0.09/hr versus RTX 4080 SUPER's 16 GB.
Higher 48.7 TFLOPS on RTX 4080 SUPER accelerates gradient updates compared to 26.7 TFLOPS on RTX 4000 Ada Generation, ideal for iterative optimization.
RTX 4080 SUPER's doubled bandwidth at 717 GB/s and compute speed up image generation pipelines over RTX 4000 Ada Generation's 360 GB/s.
RTX 4000 Ada Generation offers 20 GB VRAM for large datasets at lower 130W TDP; RTX 4080 SUPER provides 48.7 TFLOPS for FP32-heavy simulations.
Frequently Asked Questions
Which GPU has more VRAM?▾
The RTX 4000 Ada Generation provides 20 GB GDDR6 VRAM, exceeding the RTX 4080 SUPER's 16 GB GDDR6X. This advantage aids memory-intensive AI models. Bandwidth remains lower at 360 GB/s versus 717 GB/s.
What are the FP32 performance differences?▾
RTX 4080 SUPER delivers 48.7 TFLOPS FP32, 82 percent above RTX 4000 Ada Generation's 26.7 TFLOPS. This boosts scientific computing and training tasks. FP16 matches this delta at identical rates per GPU.
How do power consumptions compare?▾
RTX 4000 Ada Generation uses 130W TDP, far below RTX 4080 SUPER's 320W. Lower power reduces cloud costs for long runs. It suits efficient inference setups.
What is the cheapest cloud pricing?▾
RTX 4000 Ada Generation starts at $0.09/hr, averaging $0.27/hr across 9 offers. RTX 4080 SUPER begins at $0.17/hr, averaging $0.32/hr over 3 offers. More availability favors RTX 4000 Ada Generation.
Which has higher memory bandwidth?▾
RTX 4080 SUPER offers 717 GB/s, double the RTX 4000 Ada Generation's 360 GB/s. This enables bigger batches in training. GDDR6X memory type contributes to the edge.
Are both on the same architecture?▾
Yes, both use Ada Lovelace: RTX 4000 Ada Generation from 2023 and RTX 4080 SUPER from 2022. Shared features ensure compatibility in ML frameworks. PCIe form factor fits cloud instances.
Which is cheaper to rent, the RTX 4000 Ada or the RTX 4080?▾
Cloud rental prices for both the RTX 4000 Ada and RTX 4080 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the RTX 4000 Ada have compared to the RTX 4080?▾
The RTX 4000 Ada has 20 GB of GDDR6 memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find RTX 4000 Ada and RTX 4080 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the RTX 4000 Ada and the RTX 4080?▾
The RTX 4000 Ada uses the Ada Lovelace architecture (2023) while the RTX 4080 uses Ada Lovelace (2022). The RTX 4080 delivers 1.8x the FP16 throughput and 2.0x the memory bandwidth of the RTX 4000 Ada.