NVIDIA RTX 4060 Ti 16GB vs NVIDIA RTX 4080
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 149% more memory bandwidth, runs 45 models natively (vs 45), and exclusively fits 0 models the other cannot.
Analysis
The RTX 4060 Ti 16GB and the RTX 4080 share an identical 16GB VRAM ceiling, so every model that fits one fits the other, and NVIDIA priced the two roughly $700 apart at launch ($499 vs $1,199) for a reason that has nothing to do with capacity.
Because both cards cap out at the same 16GB, this site's calculator returns a fits-or-offload verdict for the same set of models on either card at any given quant; the RTX 4080's advantage is speed, not access. The RTX 4060 Ti 16GB's AD106 die runs a narrow 128-bit bus at 288 GB/s, while the RTX 4080's AD103 die runs a 256-bit bus at 717 GB/s, 2.49x the bandwidth. Since decode is bandwidth-bound, that ratio shows up almost exactly in this site's calculator: Llama 3.1 8B at its recommended Q5_K_M decodes at 68.8 tok/s on the RTX 4080 versus 27.7 tok/s on the RTX 4060 Ti 16GB, a 2.48x gap, and GPT-OSS 20B at its recommended Q4_K_M reaches 62 tok/s versus 24.9 tok/s, a 2.49x gap, both tracking the bandwidth ratio to within a rounding error. Qwen 3.6 27B is the one case where the RTX 4060 Ti 16GB's narrower bus doesn't cost it a native fit, just speed: both cards hold the model at Q3_K_M (15.15 GB), decoding at 34.5 tok/s on the RTX 4080 versus 13.8 tok/s on the RTX 4060 Ti 16GB.
Bottom line: This is a pure speed-for-money tradeoff with no capability difference: whatever fits the RTX 4060 Ti 16GB's VRAM fits the RTX 4080's identical 16GB too, just at roughly 2.5x the tok/s for roughly 2.4x the launch price. Anyone whose workload is throughput-sensitive, batch generation or multi-turn chat where waiting matters, should pay for the RTX 4080's bandwidth. Anyone who just needs a specific model class to fit at all, and can tolerate a slower generation speed, gets identical model compatibility for well under half the price on the RTX 4060 Ti 16GB.
Specs comparison
| Spec | NVIDIA RTX 4060 Ti 16GB | NVIDIA RTX 4080 |
|---|---|---|
| VRAM | 16 GB | 16 GB |
| Memory type | GDDR6 | GDDR6X |
| Bandwidth | 288 GB/s | 717 GB/s(+149%) |
| Architecture | Ada Lovelace | Ada Lovelace |
| Backend | CUDA | CUDA |
| Tier | Consumer | Consumer |
| Released | 2023 | 2022 |
| Models (native) | 45 | 45 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4060 Ti 16GB | NVIDIA RTX 4080 | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | N/A | N/A | N/A |
| Qwen 3.6 27B(27B) | 13.8 t/s(Q3_K_M) | 34.5 t/s(Q3_K_M) | -60% |
| Llama 3.1 8B Instruct(8B) | 19.5 t/s(Q8_0) | 48.7 t/s(Q8_0) | -60% |
| Qwen 2.5 7B Instruct(7.6B) | 21.9 t/s(Q8_0) | 54.5 t/s(Q8_0) | -60% |
Delta is NVIDIA RTX 4060 Ti 16GB relative to NVIDIA RTX 4080.
Only NVIDIA RTX 4060 Ti 16GB can run(0)
No exclusive models: NVIDIA RTX 4080 can run everything NVIDIA RTX 4060 Ti 16GB can.
Only NVIDIA RTX 4080 can run(0)
No exclusive models: NVIDIA RTX 4060 Ti 16GB can run everything NVIDIA RTX 4080 can.
Both run natively(45)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 35B-A3B (MoE)47.1 t/svs117.2 t/s
- Ornith 1.5 35B-A3B (MoE)47.1 t/svs117.2 t/s
- Nemotron 3 Nano 30B44.1 t/svs109.8 t/s
- Gemma 4 31B14.2 t/svs35.3 t/s
- Qwen3 30B-A3B (MoE)40.6 t/svs101 t/s
- Nemotron 3.5 Lightning 30B-A3B48.5 t/svs120.7 t/s
- Muse Glimmer 30B13.8 t/svs34.4 t/s
- Gemma 2 27B Instruct13.9 t/svs34.7 t/s
- Gemma 3 27B Instruct15.8 t/svs39.4 t/s
- Qwen 3.6 27B13.8 t/svs34.5 t/s
- UI-Mate 27B13.8 t/svs34.5 t/s
- Bonsai 27B21.2 t/svs52.9 t/s
- Qwen 3.8 27B13.8 t/svs34.5 t/s
- Gemma 4 26B (MoE)28.9 t/svs72 t/s
- Mistral Small 3.1 24B Instruct14.5 t/svs36.2 t/s
- Mistral Small 22B14.9 t/svs37.1 t/s
- +29 more on both
Which should you choose?
- • You want the newer architecture and longer driver support lifecycle
- • Faster token generation is the priority
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX 4060 Ti 16GB or NVIDIA RTX 4080?
- For local AI inference, the NVIDIA RTX 4080 has the edge. It offers 16 GB VRAM (vs 16 GB) and 717 GB/s bandwidth (vs 288 GB/s), letting it run 45 models natively in VRAM vs 45 for its rival.
- How much VRAM does the NVIDIA RTX 4060 Ti 16GB have vs the NVIDIA RTX 4080?
- The NVIDIA RTX 4060 Ti 16GB has 16 GB of GDDR6 at 288 GB/s. The NVIDIA RTX 4080 has 16 GB of GDDR6X at 717 GB/s. Both GPUs have the same VRAM amount; bandwidth determines which generates tokens faster.
- Can the NVIDIA RTX 4060 Ti 16GB run Llama 3.3 70B?
- The NVIDIA RTX 4060 Ti 16GB can run Llama 3.3 70B with CPU offload at Q3_K_M, but at reduced speed.
- 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.
- What is the difference between the NVIDIA RTX 4060 Ti 16GB and NVIDIA RTX 4080 for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 4060 Ti 16GB has 16 GB VRAM at 288 GB/s (CUDA backend). The NVIDIA RTX 4080 has 16 GB VRAM at 717 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 4060 Ti 16GB runs 45 models natively vs 45 for the NVIDIA RTX 4080.