NVIDIA RTX 5060 Ti 16GB
The NVIDIA RTX 5060 Ti 16GB has 16 GB VRAM and 448 GB/s memory bandwidth. It can run 38 of our 84 tracked models natively in VRAM at 8k context.
With 16 GB GDDR7, the NVIDIA RTX 5060 Ti 16GB is a consumer-tier GPU that can run 38 models natively. It handles smaller models (7B–14B) at Q4–Q5 quantization.
The NVIDIA RTX 5060 Ti 16GB is an unusual SKU — it has more VRAM than the RTX 5070 (16GB vs 12GB) but on a narrower 128-bit bus (448 GB/s). With 4,608 CUDA cores, it can hold larger models in memory than the 5070 but runs them slower due to bandwidth constraints. A practical option for budget local LLM experimentation with 14B models.
NVIDIA RTX 5060 Ti 16GB: May 2025 Blackwell GB206 die with 16GB GDDR7 on a narrower 128-bit bus at 448 GB/s — $429 MSRP.
7B-14B models fit natively at Q4. Holds larger models than the 5070 thanks to its 16GB capacity, but the narrower bus caps throughput. ~8-12 t/s for 7B Q4.
Full CUDA support. The budget pick when VRAM capacity matters more than raw bandwidth.
| Vendor | NVIDIA |
| Architecture | Blackwell |
| VRAM | 16 GB |
| Memory type | GDDR7 |
| Memory bandwidth | 448 GB/s |
| Compute backend | CUDA |
| Tier | Consumer |
| Released | 2025 |
| Models (native) | 38 / 84 |
| Models (offload) | 13 / 84 |
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 (38)
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q2_K · ~73.2 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q2_K · ~68.6 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q2_K · ~63.1 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q2_K · ~21.7 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q2_K · ~24.6 t/s
Show 33 more
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q3_K_M · ~21.5 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~33.1 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6NVFP4 · ~43.4 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8NVFP4 · ~21.8 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2NVFP4 · ~22.4 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9NVFP4 · ~46.9 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0NVFP4 · ~33.3 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7NVFP4 · ~32.5 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4NVFP4 · ~34.9 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6NVFP4 · ~39.1 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6NVFP4 · ~40.7 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2NVFP4 · ~31.6 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0NVFP4 · ~39.3 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5NVFP4 · ~61.1 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3NVFP4 · ~57.4 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0NVFP4 · ~57.4 t/s
- Qwen3 8B8B · MMLU-Pro 56.7NVFP4 · ~55.9 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3NVFP4 · ~68.2 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0NVFP4 · ~62 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~34.2 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~32.3 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~26.9 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~33.6 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~39.7 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~22.9 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~25.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~34.7 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~34.6 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~46.7 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~55.7 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~67.3 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~138.6 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~164 t/s
Models that fit with CPU offload (13)
These use system RAM for layers that don't fit in VRAM — 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 29.7NVFP4 · ~2.6 t/s
- Command-R 35B35B · MMLU-Pro 33.0NVFP4 · ~1.7 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2NVFP4 · ~4.3 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0NVFP4 · ~4.7 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5NVFP4 · ~6.5 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0NVFP4 · ~5.6 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4NVFP4 · ~5.6 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0NVFP4 · ~5.6 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2NVFP4 · ~5.2 t/s
Too large for this GPU (33)
- 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
Continue reading
Frequently asked questions
- How much VRAM does the NVIDIA RTX 5060 Ti 16GB have?
- The NVIDIA RTX 5060 Ti 16GB has 16 GB of GDDR7 with 448 GB/s memory bandwidth.
- What is the NVIDIA RTX 5060 Ti 16GB best for?
- With 16 GB of VRAM, the NVIDIA RTX 5060 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 5060 Ti 16GB run locally?
- The NVIDIA RTX 5060 Ti 16GB can run 38 of the 84 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at NVFP4, Llama 3.2 3B Instruct at BF16, Llama 3.2 1B Instruct at FP32.
- Can the NVIDIA RTX 5060 Ti 16GB run Llama 3.3 70B Instruct?
- The NVIDIA RTX 5060 Ti 16GB can run Llama 3.3 70B Instruct with CPU offload at Q3_K_M quantization, but inference will be slower than native VRAM execution.
- Can the NVIDIA RTX 5060 Ti 16GB run Qwen 3.6 27B?
- Yes. The NVIDIA RTX 5060 Ti 16GB runs Qwen 3.6 27B natively in VRAM at Q3_K_M quantization, achieving approximately 21.5 tokens per second.
- Can the NVIDIA RTX 5060 Ti 16GB run Llama 3.1 8B Instruct?
- Yes. The NVIDIA RTX 5060 Ti 16GB runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 57.4 tokens per second.