Specifications Compared
| Spec | RTX-4070 | RTX-4080 |
|---|---|---|
| TDP | 200W | 320W |
| VRAM | 12 GB | 16 GB |
| CUDA Cores | 5,888 | 9,728 |
| Memory Type | GDDR6X | GDDR6X |
| Architecture | Ada Lovelace | Ada Lovelace |
| Form Factors | PCIe | PCIe |
| Interconnect | ||
| Tensor Cores | 184 | 304 |
| FP16 Performance | 29.1 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 29.1 TFLOPS | 48.7 TFLOPS |
| INT8 Performance | 466 TOPS | 780 TOPS |
| Memory Bandwidth | 504 GB/s | 717 GB/s |
Performance Analysis
Compute performance favors the RTX 4080: its 48.7 TFLOPS in FP16 and FP32 exceeds the RTX 4070 SUPER's 35.5 TFLOPS by 37 percent, translating to faster training epochs and inference queries in deep learning pipelines. For training large language models, this delta reduces wall-clock time significantly. The 4080's 717 GB/s memory bandwidth, versus 504 GB/s, enables 42 percent larger batch sizes before memory limits, minimizing overhead from gradient accumulation. FP16/FP32 parity on both GPUs supports mixed-precision workflows without precision loss. VRAM capacity cements the gap: 16 GB on the 4080 accommodates models up to 13 billion parameters at FP16, while 12 GB on the 4070 SUPER suits up to 7 billion parameters. Higher 320W TDP on the 4080 demands more cooling and power in cloud environments, potentially raising operational costs over the 4070 SUPER's 220W.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4070 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
RTX 4080
| 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 4070 SUPER
The RTX 4070 SUPER excels in power-constrained or budget-sensitive deployments. Its 220W TDP fits smaller cloud instances or edge setups, delivering 35.5 TFLOPS for efficient Stable Diffusion generation or fine-tuning models under 12 GB VRAM. Users prioritizing cost per FLOP over peak throughput select it for inference serving where 504 GB/s bandwidth suffices for moderate batch sizes.
When to Choose the RTX 4080
Opt for the RTX 4080 in demanding AI workloads requiring scale. The 16 GB VRAM and 717 GB/s bandwidth handle large-batch LLM training or high-resolution diffusion models without fragmentation issues. At 48.7 TFLOPS, it processes complex scientific simulations 37 percent faster than the 4070 SUPER, justifying the 320W TDP for high-utilization cloud rentals starting at $0.11 per hour.
Use Cases
RTX 4080's 16 GB VRAM and 717 GB/s bandwidth support larger models and batch sizes during training. Its 48.7 TFLOPS accelerates convergence compared to 4070 SUPER's 12 GB and 35.5 TFLOPS.
Higher 48.7 TFLOPS and 16 GB VRAM on RTX 4080 enable faster token generation for larger LLMs. 4070 SUPER's 12 GB limits context lengths in demanding deployments.
Both GPUs handle fine-tuning of 7-13B parameter models effectively. RTX 4070 SUPER's lower 220W TDP suits cost-sensitive runs, while 4080 offers speed via 48.7 TFLOPS.
RTX 4070 SUPER's 12 GB VRAM and 504 GB/s bandwidth suffice for high-resolution image generation at 35.5 TFLOPS. Lower 220W TDP reduces cloud expenses for creative workflows.
RTX 4080's 48.7 TFLOPS FP32 and 717 GB/s bandwidth speed up simulations with large datasets. Extra 4 GB VRAM prevents out-of-memory errors in memory-heavy computations.
Frequently Asked Questions
Which has more VRAM: RTX 4070 SUPER or RTX 4080?▾
The RTX 4080 provides 16 GB GDDR6X VRAM, exceeding the RTX 4070 SUPER's 12 GB. This allows the 4080 to load larger AI models without quantization. Bandwidth follows suit at 717 GB/s versus 504 GB/s.
RTX 4070 SUPER vs 4080: which is faster for AI?▾
RTX 4080 delivers 48.7 TFLOPS FP16/FP32, 37 percent above 4070 SUPER's 35.5 TFLOPS. This boosts training and inference speeds in TensorFlow or PyTorch. Real-world benchmarks confirm 30-40 percent gains on large models.
What is the power consumption difference?▾
RTX 4070 SUPER has a 220W TDP, lower than RTX 4080's 320W. Lower TDP reduces cloud electricity costs and heat output. Both fit PCIe slots without modifications.
RTX 4080 cloud pricing?▾
Cloud offers for RTX 4080 start at $0.11 per hour, averaging $0.26 per hour across five providers. RTX 4070 SUPER lacks current live cloud listings. Prices vary by region and commitment.
Can RTX 4070 SUPER handle LLM inference?▾
Yes, its 12 GB VRAM and 35.5 TFLOPS support inference on 7B parameter LLMs at FP16. For larger models, RTX 4080's 16 GB is preferable. Batch size maxes at 504 GB/s bandwidth limit.
Same architecture?▾
Both use Ada Lovelace: 4070 SUPER from 2024, 4080 from 2022. Shared features include DLSS and ray tracing cores. Compute specs differ as noted in TFLOPS and VRAM.
Which is cheaper to rent, the RTX 4070 or the RTX 4080?▾
Cloud rental prices for both the RTX 4070 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 4070 have compared to the RTX 4080?▾
The RTX 4070 has 12 GB of GDDR6X memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find RTX 4070 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 4070 and the RTX 4080?▾
The RTX 4070 uses the Ada Lovelace architecture (2023) while the RTX 4080 uses Ada Lovelace (2022). The RTX 4080 delivers 1.7x the FP16 throughput and 1.4x the memory bandwidth of the RTX 4070.