NVIDIA RTX 4060 Ti 16GB
The NVIDIA RTX 4060 Ti 16GB has 16 GB VRAM and 288 GB/s memory bandwidth. It can run 45 of our 97 tracked models natively in VRAM at 8k context.
With 16 GB GDDR6, the NVIDIA RTX 4060 Ti 16GB is a consumer-tier GPU that can run 45 models natively. 7B-14B at Q4 native. 27-32B at Q4 with CPU offload. ~8-12 t/s for 7B despite lower bandwidth.
The NVIDIA RTX 4060 Ti 16GB is the high-VRAM variant of the 4060 Ti, featuring 16GB GDDR6 on a 128-bit bus at 288 GB/s. The 16GB capacity lets it hold 14B models at Q4_K_M, but the narrow bus limits inference throughput compared to wider-bus 16GB cards like the RTX 4080. A budget-friendly option for users who prioritize VRAM capacity over speed.
NVIDIA RTX 4060 Ti 16GB: 16GB GDDR6 at 288 GB/s, bandwidth is limited but capacity is king for LLM.
7B-14B at Q4 native. 27-32B at Q4 with CPU offload. ~8-12 t/s for 7B despite lower bandwidth.
Full CUDA support. Best budget 16GB option; bandwidth limits throughput but not model compatibility.
| Vendor | NVIDIA |
| Architecture | Ada Lovelace |
| VRAM | 16 GB |
| Memory type | GDDR6 |
| Memory bandwidth | 288 GB/s |
| Compute backend | CUDA |
| Tier | Consumer |
| Released | 2023 |
| Models (native) | 45 / 97 |
| Models (offload) | 12 / 97 |
Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.
Popular models for this GPU
Models this GPU runs natively in VRAM (45)
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q2_K · ~47.1 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ2_K · ~47.1 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q2_K · ~44.1 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q2_K · ~14.2 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q2_K · ~40.6 t/s
Show 40 more
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q2_K · ~48.5 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ3_K_M · ~13.8 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q2_K · ~13.9 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q2_K · ~15.8 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q3_K_M · ~13.8 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q3_K_M · ~13.8 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~21.2 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ3_K_M · ~13.8 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q3_K_M · ~28.9 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q3_K_M · ~14.5 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q3_K_M · ~14.9 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q4_K_M · ~24.9 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q6_K · ~13.9 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q5_K_M · ~15.5 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q6_K · ~14.6 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q6_K · ~16.5 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q6_K · ~16.9 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q6_K · ~14.3 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0Q8_0 · ~14.9 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5Q8_0 · ~19 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/AQ8_0 · ~19 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3Q8_0 · ~19.5 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0Q8_0 · ~19.5 t/s
- Qwen3 8B8B · MMLU-Pro 56.7Q8_0 · ~19.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3Q8_0 · ~21.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0Q8_0 · ~21.3 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~22 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~20.8 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~17.3 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~21.6 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~25.5 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~28.8 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~30.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~42.5 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~37.4 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~57.9 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~68.1 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~80.4 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~170.1 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~177.3 t/s
Models that fit with CPU offload (12)
These use system RAM for layers that don't fit in VRAM, so expect much slower inference.
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~1.5 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q3_K_M · ~1.1 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q3_K_M · ~1.1 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q3_K_M · ~1.1 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q5_K_M · ~1.3 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q5_K_M · ~1.1 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q6_K · ~1.4 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q6_K · ~1.5 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~1.1 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~1.1 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q8_0 · ~1.1 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~1.1 t/s
Too large for this GPU (40)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- Mistral Medium 3.5 128B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- DeepSeek V4 Flash 0731 284B
- Qwen3.8 2.4T-A95B
- Qwen3.8-Flash-Next
- DeepSeek V4 Pro 0813 1.6T
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
Compare NVIDIA RTX 4060 Ti 16GB with other GPUs
Continue reading
vram-guides9 min
Best LLMs for 16 GB VRAM GPUs (2026)hardware11 min
Best GPU for Running LLMs Locally in 2026vram-guides9 min
Mistral & Mixtral Family: Complete VRAM Referencehardware10 min
How to Choose Your First GPU for Local LLMshardware9 min
Best GPUs Under $500 for LLMs (2026)hardware10 min
NVIDIA RTX 40 Series for LLMs: Complete VRAM & Performance GuideFrequently asked questions
- How much VRAM does the NVIDIA RTX 4060 Ti 16GB have?
- The NVIDIA RTX 4060 Ti 16GB has 16 GB of GDDR6 with 288 GB/s memory bandwidth.
- What is the NVIDIA RTX 4060 Ti 16GB best for?
- With 16 GB of VRAM, the NVIDIA RTX 4060 Ti 16GB handles smaller models (7B–14B) at Q4–Q5 quantization, ideal for entry-level local LLM experimentation and lightweight inference.
- What LLMs can the NVIDIA RTX 4060 Ti 16GB run locally?
- The NVIDIA RTX 4060 Ti 16GB can run 45 of the 97 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q3_K_M, Ornith 1.5 35B-A3B (MoE) at Q2_K, Ornith 1.5 9B at Q8_0.
- Can the NVIDIA RTX 4060 Ti 16GB run Gemma 4 31B?
- Yes. The NVIDIA RTX 4060 Ti 16GB runs Gemma 4 31B natively in VRAM at Q2_K quantization, achieving approximately 14.2 tokens per second.
- Can the NVIDIA RTX 4060 Ti 16GB run Qwen 3.6 27B?
- Yes. The NVIDIA RTX 4060 Ti 16GB runs Qwen 3.6 27B natively in VRAM at Q3_K_M quantization, achieving approximately 13.8 tokens per second.
- Can the NVIDIA RTX 4060 Ti 16GB run Qwen3 8B?
- Yes. The NVIDIA RTX 4060 Ti 16GB runs Qwen3 8B natively in VRAM at Q8_0 quantization, achieving approximately 19.3 tokens per second.