The RTX 5060 Ti 16GB is interesting for local AI because of its memory: 16 GB of GDDR7 at 448 GB/s on a 128-bit bus. That is enough to run 14B models with room for context and to load gpt-oss-20b, which 12 GB cards cannot do. Its bandwidth is only moderate, so token generation is slower than on a 4070 Super or 4090.
Check your own setup with the Can My GPU Run This AI Model? calculator. The numbers below come from the same data.
RTX 5060 Ti 16GB specs that matter for local AI
- Memory: 16 GB GDDR7 on a 128-bit bus
- Memory bandwidth: 448 GB/s
- Board power: 180 W
- Watch out: an 8 GB version of the RTX 5060 Ti exists with the same 448 GB/s bandwidth. Check the memory size before you buy.
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 5060 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 | 109 to 146 tok/s |
| Llama 3.1 8B Instruct | 4.9 GB | 1.1 GB | 7.0 GB | Fits | 55 to 73 tok/s |
| Qwen3 8B | 5.0 GB | 1.2 GB | 7.2 GB | Fits | 54 to 71 tok/s |
| Gemma 3 12B Instruct | 7.5 GB | 0.9 GB | 9.3 GB | Fits | 36 to 48 tok/s |
| Qwen3 14B | 9.0 GB | 1.3 GB | 11.4 GB | Fits | 30 to 40 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 5060 Ti 16GB
- Qwen3 14B (Q4_K_M): about 11.4 GB at 8K, with room left for longer context.
- gpt-oss-20b: about 14 GB at 8K context. Its KV cache is very small, so longer context costs little extra memory.
- Gemma 3 12B: about 9.3 GB, leaving room for higher-quality quantization.
- 8B models at Q8_0: about 10.6 GB at 8K, for close-to-original quality.
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. For those you need a 24 GB card.
A good value 16 GB card for 8B to 20B models. It trades some speed for memory, which is usually the right trade for local AI. If you want 24B to 32B models, plan for 24 GB instead.
Verdict
What speed to expect
At 448 GB/s an 8B Q4 model has a ceiling near 91 tokens per second, so expect about 55 to 73 tokens per second for Llama 3.1 8B and 30 to 40 for a 14B model. We did not find a reliable measured result for this card, so these are bandwidth-based estimates and could be off by 10 to 20 percent either way.
Frequently asked questions
Can the RTX 5060 Ti 16GB run gpt-oss-20b?
Yes. The weights need about 12.8 GB at Q4-class size, with a very small KV cache, so the total is about 14 GB.
Is 16 GB enough for a 27B model?
No, not at Q4. 27B models need about 19 GB at 8K context.
Is the 8GB RTX 5060 Ti good for AI?
It has the same bandwidth but half the memory, so it is limited to about 8B models at Q4, like other 8 GB cards.
Sources and method
- RTX 5060 Ti specs: PNY’s NVIDIA GeForce linecard, January 2026 (16 GB, GDDR7, 448 GB/s, 180 W).
- 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.
