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 99 open-weight models.
Quick verdict
NVIDIA RTX 4080 is faster for local AI inference, but only on memory bandwidth (149.0% more). Both GPUs have 16 GB VRAM and run the exact same 46 models natively; neither fits anything the other can't.
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 in this site's calculator in compressed form: Llama 3.1 8B at its recommended Q5_K_M decodes at 79.1 tok/s on the RTX 4080 versus 34.9 tok/s on the RTX 4060 Ti 16GB, a 2.27x gap, and GPT-OSS 20B at its recommended Q4_K_M reaches 173.4 tok/s versus 87.5 tok/s, a 1.98x gap, both below the 2.49x bandwidth ratio because fixed per-layer costs do not shrink with faster memory. 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 39.6 tok/s on the RTX 4080 versus 17.5 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 2x to 2.3x 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) | 46 | 46 |
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) | 17.5 t/s(Q3_K_M) | 39.6 t/s(Q3_K_M) | -56% |
| Llama 3.1 8B Instruct(8B) | 25.2 t/s(Q8_0) | 58.5 t/s(Q8_0) | -57% |
| Qwen 2.5 7B Instruct(7.6B) | 28.2 t/s(Q8_0) | 65.7 t/s(Q8_0) | -57% |
Delta is NVIDIA RTX 4060 Ti 16GB relative to NVIDIA RTX 4080, compared at the highest quantization that fits natively on both cards.
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(46)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 35B-A3B (MoE)118.3 t/svs186.7 t/s
- Qwen 3.6 35B-A3B (MoE)118.3 t/svs186.7 t/s
- Ornith 1.5 35B-A3B (MoE)118.3 t/svs186.7 t/s
- Nemotron 3 Nano 30B111.5 t/svs162.5 t/s
- Gemma 4 31B18 t/svs40.8 t/s
- Qwen3 30B-A3B (MoE)86 t/svs142.4 t/s
- Muse Glimmer 30B17.7 t/svs40.6 t/s
- Gemma 2 27B Instruct17.9 t/svs41.5 t/s
- Gemma 3 27B Instruct20.2 t/svs46.5 t/s
- Qwen 3.6 27B17.5 t/svs39.6 t/s
- UI-Mate 27B17.5 t/svs39.6 t/s
- Bonsai 27B29.1 t/svs62.1 t/s
- Bonsai 2 27B34.2 t/svs71.2 t/s
- Qwen 3.8 27B17.5 t/svs39.6 t/s
- Gemma 4 26B (MoE)89.6 t/svs167.9 t/s
- Mistral Small 3.1 24B Instruct18.8 t/svs43.8 t/s
- +30 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 46 models natively in VRAM vs 46 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 46 models natively vs 46 for the NVIDIA RTX 4080.