The RTX 4060 Ti 16GB is a trade-off card: it has 16 GB of GDDR6, which opens up 14B and 20B models, but only 288 GB/s of bandwidth on a 128-bit bus, so generation is slower than on cards with wider memory.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 4060 Ti 16GB specs that matter for local AI
- Memory: 16 GB GDDR6 on a 128-bit bus
- Memory bandwidth: 288 GB/s
- Board power: 165 W
- Same bandwidth as the 8GB model: PCWorld notes the 8 GB and 16 GB versions differ only in capacity.
VRAM decides which models fit. Bandwidth decides how fast they generate text, because every new token means reading the whole model from memory.
Which AI models fit on the RTX 4060 Ti 16GB?
The table assumes Q4_K_M quantization (about 4.9 bits per weight) and an 8K context. Totals include the KV cache and about 1 GB of runtime overhead. Speed is an estimate: memory bandwidth divided by model size, at 60 to 80 percent efficiency, which matches published llama.cpp results on several RTX cards.
| Model | Weights | KV cache (8K) | Total (est.) | On 16 GB | Speed (est.) |
|---|---|---|---|---|---|
| Qwen3 4B | 2.5 GB | 1.2 GB | 4.7 GB | Fits | 70 to 94 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 35 to 47 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 34 to 46 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 23 to 31 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Fits | 19 to 25 tok/s |
| OpenAI gpt-oss-20b | 12.8 GB | 0.2 GB | 14.0 GB | Fits | Varies (MoE) |
| Mistral Small 3.2 24B | 14.7 GB | 1.3 GB | 17.0 GB | No | – |
| Qwen3.6 27B | 17.0 GB | 0.5 GB | 18.6 GB | No | – |
| Gemma 3 27B Instruct | 16.8 GB | 1.1 GB | 18.9 GB | No | – |
| Qwen3 32B | 20.1 GB | 2.1 GB | 23.2 GB | No | – |
| Qwen3.6 35B-A3B | 22.0 GB | 0.2 GB | 23.2 GB | No | – |
“Fits” means the estimate is at most 92 percent of VRAM. “Tight” means it is between 92 and 100 percent, so close other GPU apps. Mixture-of-Experts models (gpt-oss-20b and Qwen3.6 35B-A3B) need their full weights in memory, but they only use part of them per token, so speed is not estimated.
Best models for the RTX 4060 Ti 16GB
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K.
- gpt-oss-20b: about 14 GB.
- Gemma 3 12B: about 9.3 GB, with a small KV cache.
- 8B models at Q8_0: about 10.6 GB at 8K.
How context length changes things
The KV cache grows with every token of context, so a model that fits at 4K can fail at 32K.
| Model (Q4_K_M) | Context | KV cache | Total (est.) | On 16 GB |
|---|---|---|---|---|
| Llama 3.1 8B Instruct | 4K | 0.54 GB | 6.5 GB | Fits |
| Llama 3.1 8B Instruct | 8K | 1.07 GB | 7.0 GB | Fits |
| Llama 3.1 8B Instruct | 16K | 2.15 GB | 8.1 GB | Fits |
| Llama 3.1 8B Instruct | 32K | 4.29 GB | 10.2 GB | Fits |
| Qwen3 14B | 4K | 0.67 GB | 10.7 GB | Fits |
| Qwen3 14B | 8K | 1.34 GB | 11.4 GB | Fits |
| Qwen3 14B | 16K | 2.68 GB | 12.7 GB | Fits |
| Qwen3 14B | 32K | 5.37 GB | 15.4 GB | Tight |
A Q8 KV cache (an option in Ollama and llama.cpp) roughly halves the cache cost if you need more context.
What does not fit
Mistral Small 3.2 24B (about 17 GB), the 27B models (about 19 GB) and Qwen3 32B (about 23 GB) need more than 16 GB at Q4.
Capacity over speed. It lets you run models that 8 GB and 12 GB cards cannot, at the cost of slow generation. If speed matters, a card with more bandwidth is a better use of money.
Verdict
What speed to expect
At 288 GB/s, an 8B Q4 model has a ceiling near 59 tokens per second. Applying the 60 to 80 percent efficiency range seen in published llama.cpp tests gives about 35 to 47 tokens per second for Llama 3.1 8B and 23 to 31 for Gemma 3 12B. We did not find a reliable measured result for this exact card, so these are estimates that could be off by 10 to 20 percent. Larger models are slower still: a 14B model lands around 19 to 25 tokens per second.
Frequently asked questions
Is the RTX 4060 Ti 16GB good for AI?
It is good for fitting models, because of the 16 GB. It is slower per token than higher-bandwidth cards.
Can it run gpt-oss-20b?
Yes: about 14 GB at 8K context.
16GB vs 8GB version for AI?
Same bandwidth, but 16 GB opens 12B to 20B models that the 8 GB version cannot run.
Sources and method
- RTX 4060 Ti specs: PCWorld’s RTX 4060 Ti launch article and the Wikipedia GeForce 40 series table.
- Linear-attention and sliding-window layers are handled with reduced KV cache; small fixed states in some newer models are not counted.
- Model architecture: each model’s published
config.jsonon Hugging Face (Llama 3.1 8B and Gemma 3 are read from the unsloth mirrors because the original repositories require a login). - VRAM and speed figures are estimates from our calculator, not our own benchmarks. Last updated 3 Nov 2026.
