RTX 4070 Ti Super vs RTX 4080 Super for AI: The $200 Question
Last updated: July 20, 2026
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The RTX 4070 Ti Super and RTX 4080 Super are NVIDIA's two 16GB Ada Lovelace cards, separated by $200 and a modest performance gap. For AI practitioners on a budget, this is one of the most important purchasing decisions: is the 4080 Super's extra performance worth $200, or does the 4070 Ti Super deliver better value per dollar?
Both cards feature 16GB of GDDR6X VRAM, meaning they handle the exact same models. The differences come down to memory bandwidth, CUDA core count, and clock speeds — all of which favor the 4080 Super, but by how much? Let's dig in.
Spec Comparison
| Specification | RTX 4070 Ti Super | RTX 4080 Super | Difference |
|---|---|---|---|
| Architecture | Ada Lovelace (2024) | Ada Lovelace (2024) | Same |
| VRAM | 16 GB GDDR6X | 16 GB GDDR6X | Equal |
| Memory Bandwidth | 672 GB/s | 717 GB/s | +7% |
| Memory Bus | 256-bit | 256-bit | Same |
| CUDA Cores | 8,448 | 10,240 | +21% |
| Tensor Cores | 264 (4th gen) | 320 (4th gen) | +21% |
| Boost Clock | ~2.61 GHz | ~2.55 GHz | -2% |
| FP16 TFLOPS | ~71 | ~88 | +24% |
| TDP | 285W | 320W | +12% |
| MSRP | $799 | $999 | +$200 |
| PCIe | 4.0 x16 | 4.0 x16 | Same |
What the numbers tell us
- Same architecture, same generation: Both cards use Ada Lovelace with 4th-gen tensor cores. No architectural advantage for either.
- Identical VRAM: 16GB GDDR6X on both. Model fit is the same.
- 21% more CUDA cores on the 4080 Super: But only 7% more bandwidth. Since LLM inference is bandwidth-bound, the actual performance gap is closer to 7-10%.
- 24% more FP16 compute on the 4080 Super: Matters for training and image generation, less so for inference.
- $200 price premium: The 4080 Super costs 25% more for 7-24% more performance, depending on workload.
VRAM: Equal at 16GB
This is the most important comparison point: both cards have identical 16GB GDDR6X VRAM. The same models fit on both cards. The same quantization is needed. The same batch sizes work. For model loading, these cards are interchangeable.
The 4070 Ti Super is actually the more interesting card here because it brought 16GB to the $799 price point. Before its release, 16GB meant spending $999+ for the 4080 or 4080 Super. The 4070 Ti Super democratized 16GB for AI practitioners on a budget.
What fits in 16GB: Llama-3.1-8B at Q4 (~5GB), Mistral 7B at Q4 (~4.5GB), SDXL FP16 (~8GB), Flux.1 Dev FP8 (~11GB). Both cards handle all of these comfortably.
The Performance Gap
The performance gap between these two cards is workload-dependent. For memory-bound tasks (LLM inference), the gap is small — about 7-10%, matching the bandwidth difference. For compute-bound tasks (image generation, training), the gap widens to 15-24%, reflecting the CUDA core and FP16 TFLOPS advantage.
This means the value proposition flips depending on what you do:
- Primarily LLM inference (llama.cpp, Ollama): The 4070 Ti Super delivers 90-93% of the 4080 Super's performance for $200 less. Best Value
- Primarily image generation (ComfyUI, A1111): The 4080 Super is meaningfully faster for batch generation. The 4070 Ti Super is still capable but ~15-20% slower.
- Training/fine-tuning (LoRA, Kohya): The 4080 Super's extra compute helps, but neither card is ideal for serious training. A used 3090 (24GB) remains the training value king.
LLM Benchmarks
| Model / Config | 4070 Ti Super | 4080 Super | Gap |
|---|---|---|---|
| Llama-3.1-8B Q4_K_M | ~108 t/s | ~118 t/s | +9% |
| Llama-3.1-8B Q8_0 | ~83 t/s | ~90 t/s | +8% |
| Llama-3.1-8B FP16 | ~55 t/s | ~60 t/s | +9% |
| Mistral 7B Q4_K_M | ~115 t/s | ~125 t/s | +9% |
| Phi-3-Mini Q4_K_M | ~210 t/s | ~230 t/s | +10% |
| Llama-3-70B Q4 (heavy offload) | ~4.1 t/s | ~4.5 t/s | +10% |
Estimates based on bandwidth scaling (672/717 ≈ 0.94x) and compute scaling. Real-world results vary.
The gap is consistent at 8-10% for LLM workloads. In practical terms, generating 1,000 tokens with Llama-3.1-8B Q4 takes about 9.3 seconds on the 4070 Ti Super vs 8.5 seconds on the 4080 Super — a difference of 0.8 seconds that most users won't notice in interactive use.
Price per token/second
The value calculation is stark: the 4070 Ti Super delivers ~108 t/s for $799 ($7.40 per t/s), while the 4080 Super delivers ~118 t/s for $999 ($8.47 per t/s). The 4070 Ti Super is 13% cheaper per unit of LLM performance.
Image Generation
| Model / Resolution | 4070 Ti Super | 4080 Super | Gap |
|---|---|---|---|
| SDXL 1024×1024 (30 steps) | ~4.2s | ~3.5s | +20% |
| SD 1.5 512×512 (20 steps) | ~1.1s | ~0.9s | +22% |
| Flux.1 Schnell 1024×1024 (4 steps) | ~4.8s | ~4.0s | +20% |
| Flux.1 Dev 1024×1024 (28 steps FP8) | ~27s | ~23s | +17% |
| Flux.1 Dev NF4 1024×1024 (28 steps) | ~32s | ~27s | +19% |
Estimates based on compute scaling ratios. Actual performance depends on framework settings.
For image generation, the gap is wider — 17-22% — because diffusion models are more compute-intensive and benefit from the 4080 Super's extra CUDA cores and higher FP16 throughput. If your primary workload is batch image generation with SDXL or Flux.1, the 4080 Super justifies more of its price premium.
Price per image (SDXL)
At 30 steps for 1024×1024 SDXL: the 4070 Ti Super generates ~857 images/hour for $799 ($0.93 per image), while the 4080 Super generates ~1,029 images/hour for $999 ($0.97 per image). The 4070 Ti Super is slightly better value even for image generation.
Price-to-Performance Analysis
| Workload | 4070 Ti Super Value | 4080 Super Value | Winner |
|---|---|---|---|
| LLM Inference (tokens/s per $) | 0.135 t/s/$ | 0.118 t/s/$ | 4070 Ti Super |
| SDXL (images/h per $) | 1.07 img/h/$ | 1.03 img/h/$ | 4070 Ti Super |
| Flux.1 Dev FP8 (images/h per $) | 0.133 img/h/$ | 0.157 img/h/$ | 4080 Super |
| FP16 TFLOPS per $ | 0.089 TFLOPS/$ | 0.088 TFLOPS/$ | ~Tie |
The 4070 Ti Super wins or ties on every metric except Flux.1 Dev FP8, where the 4080 Super's compute advantage tips the balance. For the vast majority of AI practitioners — especially those focused on LLM inference — the 4070 Ti Super is the smarter buy.
Who Should Buy Which?
Buy the RTX 4070 Ti Super if:
- Your primary workload is LLM inference (llama.cpp, Ollama, LM Studio)
- You want the best price-to-performance for 16GB VRAM
- You're building a budget AI workstation and every dollar matters
- You do occasional image generation but it's not your main use case
- You want 16GB without spending $999
Buy the RTX 4080 Super if:
- You generate images professionally and need maximum throughput
- You run mixed workloads (LLM + image gen + training)
- The $200 difference is acceptable in your budget
- You want headroom for future, more compute-intensive AI workloads
- You plan to fine-tune small models (7B) and need faster iteration
Final Verdict
The RTX 4070 Ti Super is the best value 16GB GPU for AI workloads in 2026. It delivers 90-93% of the 4080 Super's LLM performance and 80-85% of its image generation throughput for 80% of the price. For most AI practitioners — especially those whose primary workload is LLM inference — the $200 savings are better spent elsewhere (more RAM, faster SSD, or put toward a second GPU down the line).
The 4080 Super only justifies its premium if your workload is compute-heavy (batch image generation, Flux.1 Dev in FP8, or LoRA training) and you value the extra throughput enough to pay $200 for a 15-20% improvement. That's a valid choice for professionals, but it's a luxury, not a necessity.
Bottom line: both cards are excellent for 16GB AI work. The 4070 Ti Super is the smarter purchase for budget-conscious builders. The 4080 Super is the better choice if you can afford it and prioritize raw throughput.
Frequently Asked Questions
Is the RTX 4070 Ti Super good enough for AI?
Yes. With 16GB VRAM and 672 GB/s bandwidth, it handles 7-8B LLMs at 100+ tokens/second and runs Flux.1 Dev in FP8. It delivers 90% of the 4080 Super's AI performance for $200 less.
Is the 4080 Super worth $200 more than the 4070 Ti Super?
For LLM inference, no — the gap is only 8-10%. For image generation and compute-heavy workloads, maybe — the gap is 15-20%. Most users get better value from the 4070 Ti Super.
Can both cards run Flux.1 Dev?
Yes. Flux.1 Dev in FP8 needs about 11GB VRAM, fitting comfortably in 16GB on both cards. NF4 quantization brings it down to ~7GB.
Which card is better for llama.cpp?
The 4080 Super is slightly faster (~9%) due to higher memory bandwidth, but both deliver excellent performance for 7-8B models. The 4070 Ti Super is the better value.
Should I buy a 4070 Ti Super or a used RTX 3090?
If you need 24GB VRAM for larger models, the used 3090 is better despite being older. If 16GB is sufficient and you want a newer, more efficient card with a warranty, the 4070 Ti Super is the safer choice.
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