Specifications Compared
| Spec | RTX-5080 | RTX-A4000 |
|---|---|---|
| TDP | 360W | 140W |
| VRAM | 16 GB | 16 GB |
| CUDA Cores | 10,752 | 6,144 |
| Memory Type | GDDR7 | GDDR6 |
| Architecture | Blackwell | Ampere |
| Form Factors | PCIe | PCIe |
| Interconnect | ||
| Tensor Cores | 336 | 192 |
| FP16 Performance | 56.3 TFLOPS | 19.2 TFLOPS |
| FP32 Performance | 56.3 TFLOPS | 19.2 TFLOPS |
| INT8 Performance | 900 TOPS | |
| Memory Bandwidth | 960 GB/s | 448 GB/s |
Performance Analysis
Compute superiority defines the RTX 5080: its 56.3 TFLOPS in FP16 and FP32 nearly triples the RTX A4000's 19.2 TFLOPS, accelerating training and inference workloads by up to 2.9 times in floating-point operations. This delta proves critical for deep learning models, where higher throughput reduces epoch times and enables larger models without proportional slowdowns.
Memory bandwidth impacts batch sizes profoundly: the RTX 5080's 960 GB/s allows processing batches over twice as large as the RTX A4000's 448 GB/s limit, minimizing data loading bottlenecks in training loops and improving GPU utilization. GDDR7 versus GDDR6 further enhances sustained performance under high memory pressure.
Power efficiency varies: the RTX A4000's 140 W TDP suits dense deployments, consuming less than half the RTX 5080's 360 W, which may elevate cooling and electricity costs in prolonged sessions. Both support PCIe form factors without interconnects, ensuring broad cloud compatibility.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 5080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 5080 16GB VRAM | 16GB | 0 vCPU 0GB RAM | 🌍global | $0.59/GPU/hr |
RTX A4000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Tallinn, Harjumaa | $0.08/GPU/hr | Available | ||
 Vast.ai | 8×NVIDIA RTX A4000 16GB VRAM | 16GB | 80 vCPU 201GB RAM 1698GB Storage | United Kingdom | $0.15/GPU/hr $1.17/hr total (8×) | Available | ||
 Hyperstack | 4×NVIDIA RTX A4000 16GB VRAM | 16GB | 16 vCPU 86GB RAM 500GB Storage | Norway | $0.15/GPU/hr $0.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA RTX A4000 16GB VRAM | 16GB | 8 vCPU 43GB RAM 200GB Storage | Norway | $0.15/GPU/hr $0.30/hr total (2×) | Available | ||
Hyperstack | NVIDIA RTX A4000 16GB VRAM | 16GB | 4 vCPU 21GB RAM 100GB Storage | Norway | $0.15/GPU/hr | Available |
When to Choose the RTX 5080
Opt for the RTX 5080 in compute-intensive scenarios: its 56.3 TFLOPS FP16 performance excels in LLM training and large-scale inference, where the RTX A4000's 19.2 TFLOPS falls short. The 960 GB/s bandwidth supports massive batch sizes critical for Stable Diffusion or scientific simulations demanding rapid data throughput.
When to Choose the RTX A4000
The RTX A4000 fits budget-conscious or low-power needs: at $0.08 per hour starting price and 140 W TDP, it handles fine-tuning or lighter inference efficiently across 28 cloud offers. Its 19.2 TFLOPS suffices for workloads not saturating the RTX 5080's higher capabilities, prioritizing availability and cost over peak speed.
Use Cases
The RTX 5080's 56.3 TFLOPS FP16 outperforms the A4000's 19.2 TFLOPS, slashing training times for large language models. Higher 960 GB/s bandwidth supports bigger batches.
RTX 5080's 56.3 TFLOPS FP32 enables faster token generation than the A4000's 19.2 TFLOPS. Bandwidth advantage aids high-concurrency serving.
Both offer 16 GB VRAM for typical fine-tuning; A4000's lower $0.08/hr cost suits light loads, while RTX 5080 accelerates with 56.3 TFLOPS.
RTX 5080's 960 GB/s bandwidth and 56.3 TFLOPS speed up image generation over A4000's 448 GB/s and 19.2 TFLOPS limits.
The RTX 5080's superior 56.3 TFLOPS FP32 handles complex simulations faster than the A4000's 19.2 TFLOPS.
Frequently Asked Questions
Which GPU has higher performance?▾
The RTX 5080 leads with 56.3 TFLOPS in FP16 and FP32, nearly three times the RTX A4000's 19.2 TFLOPS. This translates to faster AI training and inference.
How do memory specs compare?▾
Both have 16 GB VRAM, but RTX 5080 uses GDDR7 at 960 GB/s bandwidth versus RTX A4000's GDDR6 at 448 GB/s. Higher bandwidth benefits large batch processing.
What are the cloud pricing differences?▾
RTX 5080 starts at $0.25 per hour (average $0.38 across 4 offers); RTX A4000 at $0.08 per hour (average $0.31 across 28 offers). A4000 offers better availability.
Which has lower power consumption?▾
RTX A4000 draws 140 W TDP, less than half the RTX 5080's 360 W. This suits power-sensitive cloud or edge deployments.
Are they suitable for the same workloads?▾
Both fit PCIe slots with 16 GB VRAM for ML tasks, but RTX 5080 excels in high-compute needs due to 56.3 TFLOPS versus 19.2 TFLOPS.
What architectures do they use?▾
RTX 5080 runs Blackwell from 2025; RTX A4000 uses Ampere from 2021. Newer architecture brings efficiency gains in the RTX 5080.
Which is cheaper to rent, the RTX 5080 or the RTX A4000?▾
Cloud rental prices for both the RTX 5080 and RTX A4000 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 5080 have compared to the RTX A4000?▾
The RTX 5080 has 16 GB of GDDR7 memory. The RTX A4000 has 16 GB of GDDR6 memory.
Can I find RTX 5080 and RTX A4000 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 5080 and the RTX A4000?▾
The RTX 5080 uses the Blackwell architecture (2025) while the RTX A4000 uses Ampere (2021). The RTX 5080 delivers 2.9x the FP16 throughput and 2.1x the memory bandwidth of the RTX A4000.