Les RTX 5070 Ti et RTX 4070 Ti Super sit in the sweet spot of NVIDIA’s lineup for AI builders — powerful enough to be genuinely useful, priced below the flagship tier. Both carry 16 Go de VRAM. The choice between them is the now-familiar Blackwell question: is faster memory worth picking the newer generation?
La réponse est courte : the 5070 Ti is the better buy for a new build, but the 4070 Ti Super is a fine card that owners should keep.
Principaux enseignements
- Les deux cartes ont 16 GO DE VRAM — the same model-size ceiling.
- The RTX 5070 Ti’s GDDR7 delivers ~896 GB/s vs the 4070 Ti Super’s ~672 GB/s — a ~33% bandwidth jump.
- That lifts LLM inference by ~15–20%; Stable Diffusion gains are smaller.
- The 5070 Ti adds FP4 support and runs at a lower 300 W TDP.
- Buy the 5070 Ti fresh; do not upgrade an existing 4070 Ti Super — the gap is too small to justify it.
En bref
| Spec | RTX 5070 Ti | RTX 4070 Ti Super |
|---|---|---|
| Architecture | Blackwell GB203 | Ada Lovelace AD103 |
| Cœurs CUDA | 8,960 | 8,448 |
| VRAM | 16 GB GDDR7 | 16 GB GDDR6X |
| Largeur de bande de la mémoire | ~896 GB/s | ~672 GB/s |
| Faible précision | FP8 + FP4 | FP8 |
| TDP | 300 W | 285 W |
| Launch price | $749 | $799 |
16 GB at a friendlier price
The appeal of this tier is simple: 16 GB of VRAM without paying flagship money. Both cards comfortably handle the local-AI mainstream:
- Lama 3 8B at 8-bit, Classe 13B modèles à 4 bits
- Diffusion stable XL et Flux.1 génération d'images
- Mise au point de la LoRA des modèles 7B-8B
Neither runs a 70B model in VRAM — that needs 24 GB or more. But for the workloads most enthusiasts actually run, 16 GB is enough, and getting it for $749–799 instead of $999+ is the whole point of this class.
Bandwidth is the real difference
The CUDA-core counts are close (8,960 vs 8,448), so shader power is similar. The meaningful change is largeur de bande de la mémoire: the 5070 Ti’s GDDR7 pushes ~896 GB/s against the 4070 Ti Super’s ~672 GB/s — a genuine ~33% gain. Because LLM token generation is memory-bound, the speedup flows through fairly directly:
| Charge de travail | RTX 5070 Ti | RTX 4070 Ti Super |
|---|---|---|
| Lama 3 8B Q4_K_M | ~108 tok/s | ~90 tok/s |
| Llama 3 13B-classe Q4 | ~66 tok/s | ~55 tok/s |
| SDXL 1024×1024 (30 étapes) | ~11 it/s | ~10 it/s |
The split is the same one seen across the Blackwell range: Inférence LLM gains the most (~15–20%) because it is bandwidth-bound, while Diffusion stable, being compute-bound with near-equal core counts, gains only a little.
FP4 and efficiency
Like the rest of the Blackwell line, the 5070 Ti adds native FP4. As of 2026 few consumer inference stacks use it, so treat it as future insurance rather than a feature you will exercise this year. The 5070 Ti is also impressively efficient — Blackwell lets it deliver more performance within a modest 300 W envelope, close to the 4070 Ti Super’s 285 W.
Choose the RTX 5070 Ti if
- You are building fresh and want the longer-lived card
- LLM inference is your main workload
- You value FP4 readiness and slightly better efficiency
Choose the RTX 4070 Ti Super if
- You find it discounted well below $700 as stock clears
- You already own one — the upgrade gap is too small
- Your workload is mostly Stable Diffusion, where the cards nearly tie
The honest mid-range advice
This tier is the value pick, but the same caveat applies as one rung up: 16 GB is a real ceiling. If you expect to push into larger models, longer contexts, or heavier fine-tuning, the jump to a 24 GB RTX 4090 unlocks far more than the speed difference between these two 16 GB cards. Inside the 16 GB class, though, the 5070 Ti is the smarter long-term choice.
FAQ
Is the RTX 5070 Ti worth it over the 4070 Ti Super for AI?
For a new build, yes — it is faster, costs slightly less at launch, and adds FP4. But it is an incremental gain, not a leap. If you already own a 4070 Ti Super, do not upgrade.
Can the RTX 5070 Ti run Llama 3 70B?
No. A 70B model at 4-bit needs roughly 40 GB, far beyond the 5070 Ti’s 16 GB. For 70B in VRAM you need an RTX 5090 or a multi-GPU build.
How much faster is the 5070 Ti for LLM inference?
About 15–20% in real workloads. The gain comes almost entirely from GDDR7’s ~33% higher memory bandwidth, since LLM token generation is memory-bound.
Is 16 GB of VRAM enough for AI in 2026?
For mainstream work — 8B–13B models, Stable Diffusion, small fine-tunes — yes. For large models or long contexts it is tight. If you expect to grow beyond that, consider a 24 GB card instead.
Verdict
Les RTX 5070 Ti is the right mid-range AI card to buy in 2026: more bandwidth, FP4 headroom, and a slightly lower price than the 4070 Ti Super it replaces. But this is evolution, not revolution — the 4070 Ti Super remains a perfectly good card, and its owners gain nothing from upgrading. Both deliver the real attraction of this tier: 16 GB of usable VRAM without flagship pricing.