The RTX 4060 Ti 16GB offers entry-level 16GB VRAM at an affordable price. Despite lower bandwidth than newer cards, its 16GB capacity allows running 14B parameter models that 12GB cards cannot. A solid choice for users who need larger models on a budget.
| GPU | VRAM | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 3060 | 12 GB | 42 tok/s | 360 GB/s | $250 |
| RTX 4060 Ti | 16 GB | 34 tok/s | 288 GB/s | $409 |
| RTX 5060 Ti | 16 GB | 51 tok/s | 448 GB/s | $430 |
| RTX 4070 Ti SUPER | 16 GB | 72 tok/s | 672 GB/s | $1,148 |
Llama / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 94/100
Perf: ~34.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
ollama run llama3.1:8b-instruct-q4_K_M
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~30.8 tok/s · first token ~0.5s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4060 Ti.
ollama run qwen3.5:9b-instruct-q4_K_M
Qwen / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 88/100
Perf: ~34.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
ollama run qwen3:8b-q4_K_M
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 90/100
Perf: ~38.1 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
ollama run mistral:7b-instruct-q4_K_M
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 85/100
Perf: ~38.1 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for coding on RTX 4060 Ti.
ollama run qwen2.5-coder:7b-q4_K_M
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 86/100
Perf: ~38.1 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
ollama run qwen2.5:7b-instruct-q4_K_M
LFM2 / 8B / Q4_K_M / ~6 GB
Best for: Local agents, tool calling, fast chat·Pop: 75/100
Perf: ~34.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for local agents, tool calling, fast chat on RTX 4060 Ti.
ollama run liquidai/lfm2:8b-a1b-instruct-q4_K_M
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 77/100
Perf: ~38.1 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for reasoning, coding on RTX 4060 Ti.
ollama run deepseek-r1-distill:qwen-7b-q4_K_M
Llama / 8B / Q5_K_M / ~8 GB
Best for: Chat, Coding·Pop: 82/100
Perf: ~29.2 tok/s · first token ~0.5s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
ollama run llama3.1:8b-instruct-q5_K_M
Gemma / 9B / Q4_K_M / ~7 GB
Best for: Chat, Coding·Pop: 81/100
Perf: ~30.8 tok/s · first token ~0.5s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4060 Ti.
ollama run gemma2:9b-instruct-q4_K_M
With 16GB VRAM, the RTX 4060 Ti can run up to 14b parameter models. Top recommendations include Llama 3.1 8B Instruct, Qwen3.5 9B Instruct, Qwen3 8B.
The RTX 4060 Ti achieves 34 tokens per second with Qwen3 8B at Q4 quantization. Smaller models run faster, larger models slower.
16GB VRAM is good for local AI. You can comfortably run up to 14b parameter models with room for KV cache. 10 of our top 10 recommended models run at full speed.
Install Ollama from ollama.com, then run models directly. For example: ollama run llama3.1:8b-instruct-q4_K_M. Ollama automatically detects your NVIDIA GPU and uses CUDA acceleration.
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.
Open ModelFit Wizard →