NVIDIA RTX 4080 vs NVIDIA RTX 4070 Ti
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 94 open-weight models.
Quick verdict
NVIDIA RTX 4080 wins for local AI inference. It has 4 GB more VRAM and 42% more memory bandwidth, runs 45 models natively (vs 30), and exclusively fits 15 models the other cannot.
Analysis
The RTX 4080 and RTX 4070 Ti launched five weeks apart in late 2022 and early 2023 at $1,199 and $799 respectively, and unlike some same-generation pairs on this site, the more expensive card genuinely moved both numbers that matter for local LLM inference: more VRAM and more bandwidth, not just more compute.
The RTX 4080 pairs its AD103-300 die (9,728 CUDA cores) with 16GB of GDDR6X on a 256-bit bus at 717 GB/s; the RTX 4070 Ti's AD104-400 die (7,680 CUDA cores) tops out at 12GB on a 192-bit bus at 504 GB/s, a 42.3% bandwidth gap on top of 4GB less capacity. Both numbers show up directly in this site's calculator. For a model both cards fit, Llama 3.1 8B at Q8_0 (10.73 GB) decodes at 48.7 tok/s on the RTX 4080 versus 34.2 tok/s on the RTX 4070 Ti, a 42.4% gain that tracks the bandwidth ratio almost exactly. The VRAM gap matters more once a model's footprint crosses 12GB: GPT-OSS 20B, a model OpenAI explicitly sized for a 16GB card, fits the RTX 4080 natively at Q4_K_M (14.55 GB, 62 tok/s) but needs CPU offload on the RTX 4070 Ti (25.23 GB, 3.6 tok/s), and Qwen 3.6 27B shows the same pattern: 34.5 tok/s natively at Q3_K_M (15.15 GB) on the RTX 4080 versus 1.3 tok/s offloaded on the RTX 4070 Ti (32.75 GB). Qwen3 14B is the one case where the RTX 4070 Ti's smaller VRAM budget forces a more aggressive quant that happens to decode faster: Q3_K_M (9.48 GB, 38.7 tok/s) on the 12GB card versus the RTX 4080's headroom to run the larger Q6_K build instead (15.11 GB, 34.5 tok/s), a reminder that this site's calculator picks the best quant that fits, not always the fastest one, when a card has room to spare.
Bottom line: The RTX 4080 is the clearly stronger card for local LLM work: a real bandwidth edge on everything both cards fit, plus a 16GB ceiling that turns GPT-OSS 20B and Qwen 3.6 27B from crawling CPU-offload builds into fast native ones. Whether the extra $400 at launch pricing is worth it depends entirely on whether a 20-27B-class model is actually part of the plan: for workloads that stay comfortably inside 12GB, the RTX 4070 Ti (now discontinued in favor of the RTX 4070 Ti SUPER, which matches the RTX 4080's 16GB at a lower price) delivers the same models at a real but survivable speed penalty.
Specs comparison
| Spec | NVIDIA RTX 4080 | NVIDIA RTX 4070 Ti |
|---|---|---|
| VRAM | 16 GB | 12 GB |
| Memory type | GDDR6X | GDDR6X |
| Bandwidth | 717 GB/s(+42%) | 504 GB/s |
| Architecture | Ada Lovelace | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Consumer | Consumer |
| Released | 2022 | 2023 |
| Models (native) | 45 | 30 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4080 | NVIDIA RTX 4070 Ti | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | N/A | N/A | N/A |
| Qwen 3.6 27B(27B) | 34.5 t/s(Q3_K_M) | N/A | N/A |
| Llama 3.1 8B Instruct(8B) | 48.7 t/s(Q8_0) | 34.2 t/s(Q8_0) | +42% |
| Qwen 2.5 7B Instruct(7.6B) | 54.5 t/s(Q8_0) | 38.3 t/s(Q8_0) | +42% |
Delta is NVIDIA RTX 4080 relative to NVIDIA RTX 4070 Ti.
Only NVIDIA RTX 4080 can run(15)
Only NVIDIA RTX 4070 Ti can run(0)
No exclusive models: NVIDIA RTX 4080 can run everything NVIDIA RTX 4070 Ti can.
Both run natively(30)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Bonsai 27B52.9 t/svs37.2 t/s
- Gemma 4 26B (MoE)72 t/svs63 t/s
- Qwen3 14B34.5 t/svs38.7 t/s
- Qwen 2.5 14B Instruct38.6 t/svs37.7 t/s
- Phi-4 14B Instruct36.3 t/svs33.2 t/s
- Mistral Nemo 12B Instruct41 t/svs32.7 t/s
- Gemma 3 12B Instruct42.1 t/svs33.6 t/s
- Gemma 4 12B (Unified)35.6 t/svs36.4 t/s
- Gemma 2 9B Instruct37 t/svs35 t/s
- Qwen 3.5 9B47.4 t/svs33.3 t/s
- Ornith 1.5 9B47.4 t/svs33.3 t/s
- Llama 3.1 8B Instruct48.7 t/svs34.2 t/s
- DeepSeek R1 Distill Llama 8B48.7 t/svs34.2 t/s
- Qwen3 8B48 t/svs33.7 t/s
- Qwen 2.5 7B Instruct54.5 t/svs38.3 t/s
- Mistral 7B Instruct v0.353.1 t/svs37.3 t/s
- +14 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 4080 or NVIDIA RTX 4070 Ti?
- For local AI inference, the NVIDIA RTX 4080 has the edge. It offers 16 GB VRAM (vs 12 GB) and 717 GB/s bandwidth (vs 504 GB/s), letting it run 45 models natively in VRAM vs 30 for its rival.
- How much VRAM does the NVIDIA RTX 4080 have vs the NVIDIA RTX 4070 Ti?
- The NVIDIA RTX 4080 has 16 GB of GDDR6X at 717 GB/s. The NVIDIA RTX 4070 Ti has 12 GB of GDDR6X at 504 GB/s. The NVIDIA RTX 4080 has 4 GB more VRAM, allowing it to run 15 models the NVIDIA RTX 4070 Ti cannot fit natively.
- Can the NVIDIA RTX 4080 run Llama 3.3 70B?
- The NVIDIA RTX 4080 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 4080 and NVIDIA RTX 4070 Ti for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 4080 has 16 GB VRAM at 717 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 4080 runs 45 models natively vs 30 for the NVIDIA RTX 4070 Ti.