RTX 5070 Ti vs RTX 4080 Super for AI Workloads
The 5070 Ti is cheaper AND faster. Is there any reason left to buy a 4080 Super? We benchmarked both across LLM inference, image generation, and real-world AI tasks.
Spec Comparison
| Specification | RTX 5070 Ti | RTX 4080 Super |
|---|---|---|
| Architecture | Blackwell | Ada Lovelace |
| VRAM | 16GB GDDR7 | 16GB GDDR6X |
| Memory Bandwidth | 896 GB/s | 717 GB/s |
| CUDA Cores | 8,960 | 10,240 |
| Boost Clock | 2.81 GHz | 2.55 GHz |
| TGP | 300W | 320W |
| MSRP | $899 | $999 |
| DLSS Support | DLSS 4 (Multi-Frame Gen) | DLSS 3 (Frame Gen) |
| DisplayPort | DP 2.1 | DP 1.4a |
On paper, the 5070 Ti looks like a lateral move — fewer CUDA cores but faster memory. In practice, the Blackwell architecture and GDDR7 bandwidth make it a clear winner for AI workloads. Memory bandwidth is the single most important spec for LLM inference, and the 5070 Ti has a 25% advantage there.
LLM Inference Performance
For large language model inference, memory bandwidth is king. The GPU needs to shuttle model weights from VRAM to compute units as fast as possible, and that's exactly where GDDR7 gives the 5070 Ti its edge.
Llama 3.1 8B (Q4)
Mistral 7B (Q4)
Llama 3.1 70B (Q4, split)
The pattern is consistent: the 5070 Ti delivers roughly 20-26% higher token throughput across all model sizes. This isn't a marginal difference — over a long inference session, that's a massive productivity gap. If you're running a local coding assistant or chatbot, the 5070 Ti feels noticeably snappier.
Both cards have 16GB of VRAM, so they can load the same models. The quantized 70B models require offloading or splitting across cards, but both handle 8B-13B models entirely in VRAM. The difference is purely in how fast tokens flow.
Image Generation Benchmarks
Image generation tells a more nuanced story. For diffusion-based models like Stable Diffusion XL, the 5070 Ti's compute improvements give it a solid lead. But for Flux and other transformer-based generators, the gap narrows.
| Task | RTX 5070 Ti | RTX 4080 Super | Winner |
|---|---|---|---|
| SDXL (1024×1024, 30 steps) | 1.8s | 2.3s | 5070 Ti |
| SD 1.5 (512×512, 20 steps) | 0.5s | 0.6s | 5070 Ti |
| Flux.1 Schnell | 4.2s | 4.5s | 5070 Ti (marginal) |
| Flux.1 Dev | 11.8s | 12.1s | 5070 Ti (marginal) |
| Batch SDXL (4 images) | 5.6s | 6.8s | 5070 Ti |
For SDXL workflows, the 5070 Ti is clearly superior — batch generation and high-res outputs benefit from both the faster memory and the improved tensor cores. Flux, which relies more on raw compute than memory bandwidth, shows only a marginal difference. If you primarily use Flux, either card works well.
Power Efficiency and Thermals
This is where the generational leap becomes obvious. The 5070 Ti draws 300W under full load compared to the 4080 Super's 320W — but the real story is efficiency per token generated.
In a typical mini-ITX build with a 240mm AIO, the 5070 Ti runs 4-6°C cooler under sustained inference workloads. The GDDR7 memory also runs cooler than GDDR6X, which has been known to hit hotspots on the 4080 Super's memory modules.
CUDA Core Count: Fewer but Faster
You might notice the 5070 Ti has fewer CUDA cores (8,960 vs 10,240). Doesn't that matter? For AI workloads, not as much as you'd think. The Blackwell architecture's improvements in tensor matrix operations, combined with the massive bandwidth uplift, more than compensate. Raw CUDA core count is a 2018-era metric for comparing AI performance. Memory bandwidth and tensor throughput per core matter far more.
In FP16 matrix multiply benchmarks (the core operation in transformer inference), the 5070 Ti delivers higher effective TFLOPs despite fewer cores. The Blackwell tensor cores process larger tile sizes per clock cycle.
Should You Buy a 4080 Super in 2026?
With the 5070 Ti available at $899, the 4080 Super at $999 makes no sense for AI workloads. The only scenarios where a 4080 Super is worth considering:
- Deep discount ($700 or below): If you find a 4080 Super on sale for significantly less than the 5070 Ti, the performance gap becomes acceptable at the price.
- Used market ($550-650): A used 4080 Super in good condition can be a value play if the 5070 Ti remains out of stock.
- You already own one: If you have a 4080 Super, upgrading to a 5070 Ti isn't worth it. The VRAM is identical and the speed bump, while real, doesn't justify a $900 spend.
The Verdict
The RTX 5070 Ti is the better card for AI — full stop.
Cheaper, faster, more efficient. The 4080 Super is a previous-generation card that hasn't received a price cut aggressive enough to stay competitive.
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Frequently Asked Questions
Is the RTX 5070 Ti better than the RTX 4080 Super for AI?
Yes. The 5070 Ti offers approximately 25% faster LLM inference thanks to higher memory bandwidth (896 GB/s vs 717 GB/s), costs $100 less at MSRP, and consumes 20W less power under load. For image generation, it leads by 15-25% on SDXL and marginally on Flux.
Can the RTX 5070 Ti run the same models as the 4080 Super?
Yes. Both cards have 16GB of VRAM, so they can load identical models. The 5070 Ti runs them faster due to GDDR7's higher bandwidth. Neither card can fit a full unquantized 70B model, but both handle quantized versions with offloading or model splitting.
Should I buy a 4080 Super in 2026?
Only at a significant discount (under $750 new, under $600 used). At MSRP, the 5070 Ti is strictly better for AI workloads. The 4080 Super remains a capable card, but its price-to-performance ratio is poor compared to the newer Blackwell option.
Does the 5070 Ti support all the same AI frameworks?
Yes. Both cards support CUDA, cuDNN, PyTorch, TensorFlow, ONNX Runtime, and all major AI frameworks. The 5070 Ti's Blackwell architecture also adds support for FP4 compute, which can accelerate certain next-generation quantized models.
Which card is better for multi-GPU setups?
Both support NVLink alternatives via PCIe. For multi-GPU inference, the 5070 Ti is preferable due to lower power draw per card (easier to stay within PSU limits) and higher per-card throughput.