NVIDIA RTX 4070 Ti SUPER vs NVIDIA RTX 4070 Ti
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 87 open-weight models.
Quick verdict
NVIDIA RTX 4070 Ti SUPER wins for local AI inference. It has 4 GB more VRAM and 33% more memory bandwidth, runs 41 models natively (vs 29), and exclusively fits 12 models the other cannot.
Analysis
NVIDIA's January 2024 Super refresh replaced the RTX 4070 Ti at the exact same $799 launch price the original card carried a year earlier, but with a real spec bump: 4GB more VRAM and a wider memory bus, not just higher clocks. For anyone deciding between the two today, mostly on the secondhand market since NVIDIA discontinued the plain 4070 Ti, the gap in local LLM capability is much larger than the identical price tag suggests.
The RTX 4070 Ti SUPER moved off the RTX 4070 Ti's AD104 die onto the larger AD103 die shared with the RTX 4080, gaining 4GB of GDDR6X (12GB to 16GB) and a wider 256-bit bus (672 GB/s, up from 504 GB/s, a 33.3% bandwidth increase) alongside 768 more CUDA cores (8,448 vs 7,680). For local LLM inference, the VRAM jump matters more than the bandwidth: this site's calculator can't even complete a fair quant-for-quant comparison on Qwen 3.6 27B, since the plain RTX 4070 Ti's 12GB can't hold the model at any quantization and falls back to a CPU-offloaded Q8_0 build at 1.3 tok/s, while the Ti SUPER's 16GB holds it natively at Q3_K_M, a meaningfully higher-quality build, at 32.3 tok/s. GPT-OSS 20B, a model OpenAI explicitly sized for a 16GB card, shows the same pattern: 3.6 tok/s offloaded on the RTX 4070 Ti versus 58.1 tok/s natively on the Ti SUPER. For models that already fit both cards' 12GB, like Llama 3.1 8B at Q8_0, the Ti SUPER still decodes faster (45.6 vs 34.2 tok/s) purely from its 33.3% bandwidth edge.
Bottom line: For local LLM use specifically, the RTX 4070 Ti SUPER is the clearly better card at an identical launch price: more VRAM that changes which models fit at all, plus meaningfully faster decode on everything both cards can run. The plain RTX 4070 Ti only makes sense today at a steep secondhand discount below the Ti SUPER's used-market price, and even then only for workloads that stay comfortably inside 12GB.
Specs comparison
| Spec | NVIDIA RTX 4070 Ti SUPER | NVIDIA RTX 4070 Ti |
|---|---|---|
| VRAM | 16 GB | 12 GB |
| Memory type | GDDR6X | GDDR6X |
| Bandwidth | 672 GB/s(+33%) | 504 GB/s |
| Architecture | Ada Lovelace | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Consumer | Consumer |
| Released | 2024 | 2023 |
| Models (native) | 41 | 29 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4070 Ti SUPER | NVIDIA RTX 4070 Ti | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | — | — | — |
| Qwen 3.6 27B(27B) | 32.3 t/s(Q3_K_M) | — | — |
| Llama 3.1 8B Instruct(8B) | 45.6 t/s(Q8_0) | 34.2 t/s(Q8_0) | +33% |
| Qwen 2.5 7B Instruct(7.6B) | 51.1 t/s(Q8_0) | 38.3 t/s(Q8_0) | +33% |
Delta is NVIDIA RTX 4070 Ti SUPER relative to NVIDIA RTX 4070 Ti.
Only NVIDIA RTX 4070 Ti SUPER can run(12)
Only NVIDIA RTX 4070 Ti can run(0)
No exclusive models — NVIDIA RTX 4070 Ti SUPER can run everything NVIDIA RTX 4070 Ti can.
Both run natively(29)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Bonsai 27B49.6 t/svs37.2 t/s
- Gemma 4 26B (MoE)67.5 t/svs63 t/s
- Qwen3 14B32.4 t/svs38.7 t/s
- Qwen 2.5 14B Instruct36.2 t/svs37.7 t/s
- Phi-4 14B Instruct34 t/svs33.2 t/s
- Mistral Nemo 12B Instruct38.5 t/svs32.7 t/s
- Gemma 3 12B Instruct39.4 t/svs33.6 t/s
- Gemma 4 12B (Unified)33.4 t/svs36.4 t/s
- Gemma 2 9B Instruct34.7 t/svs35 t/s
- Qwen 3.5 9B44.4 t/svs33.3 t/s
- Llama 3.1 8B Instruct45.6 t/svs34.2 t/s
- DeepSeek R1 Distill Llama 8B45.6 t/svs34.2 t/s
- Qwen3 8B45 t/svs33.7 t/s
- Qwen 2.5 7B Instruct51.1 t/svs38.3 t/s
- Mistral 7B Instruct v0.349.7 t/svs37.3 t/s
- Gemma 3 4B Instruct51.4 t/svs38.5 t/s
- +13 more on both
Which should you choose?
- • You need to run larger models (>12 GB VRAM)
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX 4070 Ti SUPER or NVIDIA RTX 4070 Ti?
- For local AI inference, the NVIDIA RTX 4070 Ti SUPER has the edge. It offers 16 GB VRAM (vs 12 GB) and 672 GB/s bandwidth (vs 504 GB/s), letting it run 41 models natively in VRAM vs 29 for its rival.
- How much VRAM does the NVIDIA RTX 4070 Ti SUPER have vs the NVIDIA RTX 4070 Ti?
- The NVIDIA RTX 4070 Ti SUPER has 16 GB of GDDR6X at 672 GB/s. The NVIDIA RTX 4070 Ti has 12 GB of GDDR6X at 504 GB/s. The NVIDIA RTX 4070 Ti SUPER has 4 GB more VRAM, allowing it to run 12 models the NVIDIA RTX 4070 Ti cannot fit natively.
- Can the NVIDIA RTX 4070 Ti SUPER run Llama 3.3 70B?
- The NVIDIA RTX 4070 Ti SUPER can run Llama 3.3 70B with CPU offload at Q3_K_M, but at reduced speed.
- Can the NVIDIA RTX 4070 Ti run Llama 3.3 70B?
- The NVIDIA RTX 4070 Ti can run Llama 3.3 70B with CPU offload at Q2_K, but at reduced speed.
- What is the difference between the NVIDIA RTX 4070 Ti SUPER and NVIDIA RTX 4070 Ti for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 4070 Ti SUPER has 16 GB VRAM at 672 GB/s (CUDA backend). The NVIDIA RTX 4070 Ti has 12 GB VRAM at 504 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 4070 Ti SUPER runs 41 models natively vs 29 for the NVIDIA RTX 4070 Ti.