Compare · GPUs
RTX 4060 Ti vs RTX 4060 Ti for local LLMs
It runs 21 of 39 models fully in memory, against 14 for the RTX 4060 Ti, thanks to 16 GB of VRAM versus 8 GB. The RTX 4060 Ti is about 25% faster on the models both can hold, so it only wins if you stick to small models.
It runs 21 of 39 models fully in memory, against 14 for the RTX 4060 Ti, thanks to 16 GB of VRAM versus 8 GB. The RTX 4060 Ti is about 25% faster on the models both can hold, so it only wins if you stick to small models.
Every model at 8K context with the best quantization that fits. Offload assumes 32 GB of system RAM. Green marks the better result. Rows where the two differ come first.
| Model | RTX 4060 Ti | RTX 4060 Ti |
|---|---|---|
| Gemma 4 12B | Runs~19 tok/sQ6_K | Offload~4.3 tok/sQ4_K_M |
| Gemma 3 12B | Runs~19 tok/sQ6_K | Offload~4.2 tok/sQ4_K_M |
| Mistral Nemo 12B | Runs~19 tok/sQ6_K | Offload~4.1 tok/sQ4_K_M |
| Phi-4 14B | Runs~18 tok/sQ5_K_M | Offload~3.5 tok/sQ4_K_M |
| Qwen3 14B | Runs~18 tok/sQ5_K_M | Offload~3.5 tok/sQ4_K_M |
| DeepSeek-R1 Distill 14B | Runs~18 tok/sQ5_K_M | Offload~3.5 tok/sQ4_K_M |
| gpt-oss 20B | Runs~39 tok/sQ4_K_M | Offload~9.5 tok/sQ4_K_M |
| Qwen3 0.6B | Runs~290 tok/sQ8_0 | Runs~290 tok/sQ8_0 |
| Gemma 3 1B | Runs~180 tok/sQ8_0 | Runs~180 tok/sQ8_0 |
| Llama 3.2 1B | Runs~150 tok/sQ8_0 | Runs~150 tok/sQ8_0 |
| Llama 3.2 3B | Runs~55 tok/sQ8_0 | Runs~55 tok/sQ8_0 |
| Phi-4 Mini 3.8B | Runs~47 tok/sQ8_0 | Runs~47 tok/sQ8_0 |
| Qwen3 4B | Runs~44 tok/sQ8_0 | Runs~44 tok/sQ8_0 |
| Gemma 3 4B | Runs~41 tok/sQ8_0 | Runs~41 tok/sQ8_0 |
| Qwen3.5 4B | Runs~38 tok/sQ8_0 | Runs~38 tok/sQ8_0 |
| Mistral 7B v0.3 | Runs~25 tok/sQ8_0 | Runs~37 tok/sQ5_K_M |
| Qwen2.5-Coder 7B | Runs~23 tok/sQ8_0 | Runs~35 tok/sQ5_K_M |
| Llama 3.1 8B | Runs~22 tok/sQ8_0 | Runs~40 tok/sQ4_K_M |
| DeepSeek-R1 Distill 8B | Runs~22 tok/sQ8_0 | Runs~40 tok/sQ4_K_M |
| Qwen3 8B | Runs~22 tok/sQ8_0 | Runs~39 tok/sQ4_K_M |
| Qwen3.5 9B | Runs~19 tok/sQ8_0 | Runs~34 tok/sQ4_K_M |
| Codestral 22B | Offload~2.2 tok/sQ4_K_M | Offload~2.4 tok/sQ4_K_M |
| Mistral Small 3 24B | Offload~2.1 tok/sQ4_K_M | Offload~2.3 tok/sQ4_K_M |
| Devstral Small 24B | Offload~2.1 tok/sQ4_K_M | Offload~2.3 tok/sQ4_K_M |
| Gemma 4 26B-A4B (MoE) | Offload~6.7 tok/sQ4_K_M | Offload~9.2 tok/sQ4_K_M |
| Gemma 3 27B | Offload~1.9 tok/sQ4_K_M | Offload~2.0 tok/sQ4_K_M |
| Qwen3.6 27B | Offload~1.8 tok/sQ4_K_M | Offload~2.0 tok/sQ4_K_M |
| Qwen3 30B-A3B (MoE) | Offload~7.9 tok/sQ4_K_M | Offload~11 tok/sQ4_K_M |
| Qwen3-Coder 30B-A3B | Offload~7.9 tok/sQ4_K_M | Offload~11 tok/sQ4_K_M |
| Gemma 4 31B | Offload~1.7 tok/sQ4_K_M | Offload~1.8 tok/sQ4_K_M |
| Qwen3 32B | Offload~1.6 tok/sQ4_K_M | Offload~1.7 tok/sQ4_K_M |
| DeepSeek-R1 Distill 32B | Offload~1.6 tok/sQ4_K_M | Offload~1.7 tok/sQ4_K_M |
| Qwen2.5-Coder 32B | Offload~1.6 tok/sQ4_K_M | Offload~1.7 tok/sQ4_K_M |
| Qwen3.6 35B-A3B (MoE) | Offload~8.8 tok/sQ4_K_M | Offload~11 tok/sQ4_K_M |
| Llama 3.3 70B | Won’t fit | Won’t fit |
| DeepSeek-R1 Distill 70B | Won’t fit | Won’t fit |
| Llama 4 Scout 109B-A17B | Won’t fit | Won’t fit |
| gpt-oss 120B | Won’t fit | Won’t fit |
| Qwen3 235B-A22B (MoE) | Won’t fit | Won’t fit |
Which should you buy?
The RTX 4060 Ti is the better card for local AI. It runs 21 of 39 models fully in memory, against 14 for the RTX 4060 Ti, thanks to 16 GB of VRAM versus 8 GB. The RTX 4060 Ti is about 25% faster on the models both can hold, so it only wins if you stick to small models.
Only the RTX 4060 Ti fits Gemma 3 12B, Mistral Nemo 12B, Phi-4 14B, Qwen3 14B, DeepSeek-R1 Distill 14B, gpt-oss 20B and Gemma 4 12B fully in memory.
See every result on its own page: RTX 4060 Ti and RTX 4060 Ti.
Questions
Is the RTX 4060 Ti or the RTX 4060 Ti better for running LLMs?
The RTX 4060 Ti is the better card for local AI. It runs 21 of 39 models fully in memory, against 14 for the RTX 4060 Ti, thanks to 16 GB of VRAM versus 8 GB. The RTX 4060 Ti is about 25% faster on the models both can hold, so it only wins if you stick to small models.
Which models can the RTX 4060 Ti run that the RTX 4060 Ti can't?
At 8K context, only the RTX 4060 Ti holds Gemma 3 12B, Mistral Nemo 12B, Phi-4 14B, Qwen3 14B, DeepSeek-R1 Distill 14B and gpt-oss 20B and 1 more fully in VRAM.
Does the RTX 4060 Ti or RTX 4060 Ti need anything else to run local AI?
Only the software. Install Ollama or LM Studio, pick a model that fits in VRAM, and it runs on the GPU. 32 GB of system RAM helps when a model spills over, but that path is much slower.
More comparisons
Estimates from memory size and bandwidth at 8K context. Real speed varies with software, drivers and settings.