Specifications Compared
| Spec | A30 | RTX-4080 |
|---|---|---|
| TDP | 165W | 320W |
| VRAM | 24 GB | 16 GB |
| CUDA Cores | 3,584 | 9,728 |
| Memory Type | HBM2 | GDDR6X |
| Architecture | Ampere | Ada Lovelace |
| Form Factors | PCIe | PCIe |
| Interconnect | NVLink | |
| Tensor Cores | 224 | 304 |
| FP16 Performance | 10.3 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 48.7 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | |
| INT8 Performance | 165 TOPS | 780 TOPS |
| Memory Bandwidth | 933 GB/s | 717 GB/s |
Performance Analysis
Compute throughput defines the primary performance gap: the RTX 4080 SUPER delivers 48.7 TFLOPS in both FP16 and FP32, exceeding the A30's 10.3 TFLOPS by a factor of 4.7. This advantage accelerates deep learning training cycles and real-time inference, allowing models to complete FP16/FP32 operations 4.7 times faster on the RTX 4080 SUPER.
Memory specifications favor the A30 for data-heavy workloads, with 24 GB HBM2 at 933 GB/s bandwidth surpassing the RTX 4080 SUPER's 16 GB GDDR6X at 717 GB/s. Superior bandwidth enables larger batch sizes in training large models, reducing data transfer bottlenecks and improving utilization. The A30 thus handles memory-bound tasks like extensive LLM fine-tuning more effectively.
TDP influences practical deployment: the A30's 165W supports higher density in clusters than the 320W RTX 4080 SUPER, lowering energy costs despite its lower peak performance.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 A30
The NVIDIA A30 proves ideal for memory-intensive applications requiring over 16 GB VRAM. Its 24 GB HBM2 and 933 GB/s bandwidth accommodate large batch sizes in LLM training or scientific simulations, preventing out-of-memory issues. The 165W TDP and NVLink support enable efficient multi-GPU setups in power-constrained data centers.
When to Choose the RTX 4080 SUPER
Select the NVIDIA GeForce RTX 4080 SUPER for compute-dominated workloads where speed trumps memory capacity. The 48.7 TFLOPS in FP16 and FP32 yield 4.7 times faster training and inference than the A30's 10.3 TFLOPS, suiting iterative fine-tuning or high-volume inference. Cloud pricing from $0.17 per hour provides strong value for models fitting within 16 GB GDDR6X.
Use Cases
The A30's 24 GB HBM2 VRAM and 933 GB/s bandwidth support larger models and batch sizes critical for LLM training. The RTX 4080 SUPER's 16 GB limits scalability despite higher 48.7 TFLOPS.
RTX 4080 SUPER's 48.7 TFLOPS in FP16 delivers 4.7 times faster inference than A30's 10.3 TFLOPS for high-throughput serving. Its 16 GB suffices for most deployed LLMs.
The 48.7 TFLOPS on RTX 4080 SUPER accelerates fine-tuning iterations 4.7 times over A30, ideal for rapid experimentation. Pricing from $0.17 per hour adds efficiency.
Ada Lovelace architecture and 48.7 TFLOPS optimize Stable Diffusion generation on RTX 4080 SUPER. The 16 GB GDDR6X handles typical image synthesis workloads effectively.
A30's 24 GB VRAM and NVLink suit memory-heavy simulations; RTX 4080 SUPER's 48.7 TFLOPS excels in FP32 compute tasks. Choice depends on data size versus speed needs.
Frequently Asked Questions
Which GPU has more VRAM?▾
The NVIDIA A30 offers 24 GB HBM2 VRAM, exceeding the RTX 4080 SUPER's 16 GB GDDR6X. This makes the A30 better for models requiring over 16 GB. Bandwidth also favors A30 at 933 GB/s over 717 GB/s.
What is the FP32 performance difference?▾
RTX 4080 SUPER achieves 48.7 TFLOPS in FP32, 4.7 times the A30's 10.3 TFLOPS. This gap speeds up training and simulations significantly. FP16 matches this ratio.
How do power draws compare?▾
The A30 consumes 165W TDP, half the RTX 4080 SUPER's 320W. Lower power aids dense cloud deployments for A30. It supports more efficient scaling.
What are the cloud prices?▾
RTX 4080 SUPER starts at $0.17 per hour, averaging $0.32 per hour across three offers. No live offers exist for A30 currently. Pricing favors RTX for availability.
Which has higher memory bandwidth?▾
A30 provides 933 GB/s with HBM2, surpassing RTX 4080 SUPER's 717 GB/s GDDR6X. This benefits large batch processing on A30. Capacity pairs with it at 24 GB versus 16 GB.
What architectures do they use?▾
A30 uses Ampere from 2021; RTX 4080 SUPER employs Ada Lovelace from 2022. Ada delivers higher 48.7 TFLOPS efficiency. Both support PCIe form factors.
Which is cheaper to rent, the A30 or the RTX 4080?▾
Cloud rental prices for both the A30 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 A30 have compared to the RTX 4080?▾
The A30 has 24 GB of HBM2 memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find A30 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 A30 and the RTX 4080?▾
The A30 uses the Ampere architecture (2021) while the RTX 4080 uses Ada Lovelace (2022). The RTX 4080 delivers 4.7x the FP16 throughput and 1.3x the memory bandwidth of the A30.