Specifications Compared
| Spec | GTX-1080 | RTX-5000-ADA |
|---|---|---|
| TDP | 180W | 250W |
| VRAM | 8-11 GB | 32 GB |
| CUDA Cores | 2,560 | 12,800 |
| Memory Type | GDDR5X | GDDR6 |
| Architecture | Pascal | Ada Lovelace |
| Form Factors | PCIe | PCIe |
| Interconnect | ||
| FP16 Performance | 8.9 TFLOPS | 65.3 TFLOPS |
| FP32 Performance | 8.9 TFLOPS | 65.3 TFLOPS |
| Memory Bandwidth | 320 GB/s | 576 GB/s |
Performance Analysis
Compute performance defines the core disparity: the RTX 5000 Ada's 65.3 TFLOPS in FP16 and FP32 dwarfs the GTX 1080's 8.9 TFLOPS, yielding approximately 7.3 times faster processing. For training, this accelerates gradient computations and epoch times in deep learning pipelines. Inference benefits similarly through higher throughput, handling more queries per second in deployment scenarios.
VRAM capacity is pivotal: 32 GB on the RTX 5000 Ada accommodates massive models like large language models, whereas 8-11 GB on the GTX 1080 limits to smaller architectures or reduced batch sizes. Memory bandwidth of 576 GB/s versus 320 GB/s minimizes latency in data transfers, enabling larger batches without bottlenecks and improving overall utilization in memory-intensive tasks such as fine-tuning or simulations.
Power draw stands at 250W for the RTX 5000 Ada and 180W for the GTX 1080, influencing cloud costs indirectly via provider efficiency, though raw specs favor the newer GPU for high-throughput workloads.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
GTX 1080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 4×NVIDIA GeForce GTX 1080 8GB VRAM | 8GB | 0 vCPU 64GB RAM 480GB Storage | Netherlands | $0.30/GPU/hr $1.20/hr total (4×) | Available | ||
LeaderGPU | 8×NVIDIA GeForce GTX 1080 Ti 11GB VRAM | 11GB | 0 vCPU 128GB RAM 480GB Storage | Netherlands | $0.60/GPU/hr $4.80/hr total (8×) | Available |
RTX 5000 Ada
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA RTX 5000 Ada Generation 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Chubbuck, Idaho | $0.55/GPU/hr | Available | ||
 RunPod | NVIDIA RTX 5000 Ada Generation 32GB VRAM | 32GB | 10 vCPU 83GB RAM | 🌍global | $0.83/GPU/hr |
When to Choose the GTX 1080
The GTX 1080 fits legacy software or lightweight tasks constrained to 8-11 GB VRAM, such as basic inference on small models or older games. Its 8.9 TFLOPS FP32 performance handles prototyping adequately, and pricing from $0.30/hr (average $0.45/hr) offers cost savings for infrequent use across 2 live offers.
When to Choose the RTX 5000 Ada
The RTX 5000 Ada dominates demanding AI workflows requiring 32 GB VRAM and 65.3 TFLOPS, including training large models or high-resolution rendering. Despite average $0.51/hr pricing across 5 offers, its 576 GB/s bandwidth supports efficient large-batch processing unavailable on older hardware.
Use Cases
The RTX 5000 Ada's 65.3 TFLOPS FP16 and 32 GB VRAM enable training of large models with big batches, far beyond the GTX 1080's 8.9 TFLOPS and 8-11 GB limits.
65.3 TFLOPS FP16 on the RTX 5000 Ada supports high-throughput serving of LLMs, while 32 GB VRAM handles full-precision models without quantization needed on the GTX 1080.
RTX 5000 Ada's 576 GB/s bandwidth and 32 GB VRAM facilitate efficient fine-tuning of billion-parameter models; GTX 1080's 320 GB/s and lower VRAM restrict scale.
The RTX 5000 Ada's 65.3 TFLOPS and 32 GB VRAM accelerate high-resolution image generation; GTX 1080 struggles with memory for advanced pipelines.
RTX 5000 Ada's 65.3 TFLOPS FP32 outperforms GTX 1080's 8.9 TFLOPS for simulations, with 576 GB/s bandwidth aiding large dataset processing.
Frequently Asked Questions
Which GPU has more VRAM: GTX 1080 or RTX 5000 Ada?▾
The RTX 5000 Ada features 32 GB GDDR6 VRAM, tripling the GTX 1080's 8-11 GB GDDR5X. This allows larger models and batch sizes in AI tasks.
How do FP32 performance levels compare?▾
RTX 5000 Ada achieves 65.3 TFLOPS FP32, versus 8.9 TFLOPS on GTX 1080, providing about 7.3 times faster scalar computations for training and simulations.
What are the current cloud pricing differences?▾
GTX 1080 rents from $0.30/hr (average $0.45/hr across 2 offers); RTX 5000 Ada from $0.25/hr (average $0.51/hr across 5 offers). Pricing favors RTX 5000 Ada at entry level.
Is memory bandwidth better on RTX 5000 Ada?▾
RTX 5000 Ada offers 576 GB/s bandwidth, 1.8 times the GTX 1080's 320 GB/s. This reduces bottlenecks in data-heavy workloads like inference.
Which has lower TDP?▾
GTX 1080 consumes 180W TDP, lower than RTX 5000 Ada's 250W. However, performance gains make the latter preferable for intensive cloud tasks.
Are both GPUs suitable for machine learning?▾
RTX 5000 Ada excels with 65.3 TFLOPS and 32 GB VRAM for modern ML; GTX 1080's 8.9 TFLOPS suits only small-scale or legacy applications.
Which is cheaper to rent, the GTX 1080 or the RTX 5000 Ada?▾
Cloud rental prices for both the GTX 1080 and RTX 5000 Ada 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 GTX 1080 have compared to the RTX 5000 Ada?▾
The GTX 1080 has 8 to 11 GB of GDDR5X memory. The RTX 5000 Ada has 32 GB of GDDR6 memory.
Can I find GTX 1080 and RTX 5000 Ada 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 GTX 1080 and the RTX 5000 Ada?▾
The GTX 1080 uses the Pascal architecture (2016) while the RTX 5000 Ada uses Ada Lovelace (2023). The RTX 5000 Ada delivers 7.3x the FP16 throughput and 1.8x the memory bandwidth of the GTX 1080.