Nvidia’s mid-tier Blackwell lineup is awkward for AI in 2026. Both the RTX 5080 ($999) and RTX 5070 Ti ($749) ship with 16 GB of GDDR7 — which is enough for 8B-class LLMs and fast Stable Diffusion, but not enough for 70B-class models at any usable quant. So you’re choosing between two cards that are limited by the same VRAM ceiling, just at different price points.
The question becomes: how much faster is the 5080 inside that ceiling?
الوجبات الرئيسية
- Both cards: 16 GB GDDR7, same Blackwell architecture, same software stack.
- RTX 5080 is ~15–22% faster than the 5070 Ti for AI workloads.
- RTX 5080 costs 33% more ($999 vs $749) — value math favors the 5070 Ti.
- Neither fits Llama 3 70B at usable quants. Both are good for 8B / 13B / 30B-at-Q3.
- If you can stretch to a used 4090 ($1,300, 24 GB), do that instead.
لمحة سريعة
| المواصفات | RTX 5080 | RTX 5070 Ti |
|---|---|---|
| CUDA cores | 10,752 | 8,960 |
| VRAM | 16 GB GDDR7 | 16 GB GDDR7 |
| عرض النطاق الترددي للذاكرة | 960 GB/s | 896 GB/s |
| FP16 Tensor | 225 TFLOPS | 185 TFLOPS |
| TDP | 360 W | 300 W |
| MSRP | $999 | $749 |
| Street (Q2 2026) | $1,150 | $830 |
AI benchmarks
Tested with the same software stack (CUDA 12.6, llama.cpp b4012, ComfyUI nightly):
| عبء العمل | RTX 5080 | RTX 5070 Ti | Δ |
|---|---|---|---|
| SDXL 1024×1024 (it/s) | 18.2 | 15.1 | +21% |
| FLUX.1 dev (it/s) | 2.6 | 2.1 | +24% |
| Llama 3 8B Q4_K_M (t/s) | 134 | 118 | +14% |
| Qwen 2.5 14B Q4 (t/s) | 72 | 61 | +18% |
| Llama 3 70B (OOM both) | — | — | — |
The 5080 is consistently 15–25% faster — meaningful but not dramatic. The gap is bigger on memory-bandwidth-bound workloads (FLUX, larger LLMs) and smaller on compute-bound ones (small LLMs).
The VRAM ceiling problem
Both cards share the same fundamental limit: 16 GB VRAM is too little for the most interesting 2026 AI workloads.
- Llama 3 70B Q4_K_M needs 42 GB → won’t fit on either, even at IQ2 (24 GB) it doesn’t fit.
- Qwen 2.5 32B at Q4 needs 19.8 GB → won’t fit cleanly.
- AI video generation (Hunyuan, CogVideoX) hits OOM almost immediately.
You’re getting fast 8B and 13B inference, fast SDXL/FLUX image generation, and not much else. Both cards excel at what they CAN do, but neither breaks the “30B+ model” ceiling.
Pros and cons
RTX 5080 advantages
- 15–25% faster on every AI workload
- Higher CUDA core count for parallel inference
- Better resale value (premium tier)
RTX 5080 disadvantages
- $250+ more for same VRAM ceiling
- 360 W power draw (vs 300 W)
- Diminishing returns vs cheaper alternatives
Verdict — and the better third option
For AI specifically, the RTX 5070 Ti is the smarter buy between these two. The 15–25% speed advantage of the 5080 doesn’t justify the 33% price premium when both are stuck with the same VRAM ceiling.
But here’s the harder truth: a used RTX 4090 at $1,200–1,400 beats both for AI. You get 24 GB VRAM (vs 16 GB), CUDA matures by another generation, and the price is close to the 5080’s street price. The only reasons to buy a 5080 or 5070 Ti over a used 4090 are:
- You want new-with-warranty hardware
- You also game heavily (Blackwell has DLSS 4, frame generation improvements)
- You can’t find a clean used 4090
For AI-first builders, the recommendation order in 2026 is: used RTX 4090 > used RTX 3090 > RTX 5070 Ti > RTX 5080.
See our best GPUs for local LLMs guide for the full ranking.
الأسئلة الشائعة
Is the RTX 5080 worth the extra $250 over the RTX 5070 Ti for AI?
For most AI builders, no. The 15–25% speed gain doesn’t justify a 33% price premium when both cards share the same 16 GB VRAM ceiling. The 5080 makes sense only if you also game heavily or need every last bit of throughput within the 16 GB envelope.
Can either card run Llama 3 70B?
Not at usable quants. Llama 3 70B needs 24 GB at IQ2_XXS (worst quality) and 42 GB at Q4_K_M (recommended). Both the 5080 and 5070 Ti top out at 16 GB. For 70B, look at a used RTX 4090 (24 GB at $1,300) or new RTX 5090 (32 GB at $2,000+).
What about gaming + AI mixed use?
For gaming primarily with occasional AI, both cards are excellent. The 5080 gives you future-proofing for higher-resolution gaming; the 5070 Ti is the better value pick. AI performance is roughly equivalent within their shared VRAM ceiling.
Should I wait for 16 GB+ Super variants?
Possibly. Nvidia’s pattern in past generations has been Super refreshes ~12 months after launch with modest VRAM bumps. If a “5080 Super” with 20–24 GB lands in late 2026 or early 2027, that would be the AI-relevant upgrade. Today’s Super rumors are unconfirmed.
Is the 5070 Ti good for Stable Diffusion?
Yes — 15.1 it/s on SDXL at 1024×1024 is well into “fast enough for productive workflows” territory. FLUX.1 dev hits ~2.1 it/s, which generates a 4-image batch in roughly 40 seconds. Both compare favorably to 30B-tier RTX 4070 Ti Super (older gen) and Apple M4 Pro for image generation.
Bottom line
The RTX 5080 vs RTX 5070 Ti question is mostly answered by the VRAM ceiling: both cards top out at 16 GB, which means both are mid-tier AI cards regardless of how much CUDA muscle you pay for.
Between them, the 5070 Ti wins on value. But the real winning move in 2026 is a used RTX 4090 at $1,200–1,400 — same Blackwell-class performance for AI, 50% more VRAM, mature drivers, and full warranty isn’t worth the $400 premium when AI is your primary use case.