CanItRun Logocanitrun.

NVIDIA RTX 5060 Ti 16GB

The NVIDIA RTX 5060 Ti 16GB has 16 GB VRAM and 448 GB/s memory bandwidth. It can run 40 of our 70 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 40 models natively. It handles 30B-class models at Q4 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.

The NVIDIA RTX 5060 Ti 16GB is a consumer-grade NVIDIA GPU based on the Blackwell architecture. Released in 2025. It features 16 GB of GDDR7 at 448 GB/s memory bandwidth. Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.

For local LLM inference, this GPU runs 40 of the 70 models we track natively in VRAM at 8K context. The largest model it handles in VRAM is Qwen 3.5 35B-A3B (MoE) (499.3 t/s at Q2_K). It handles smaller models up to ~7-14B at reasonable precision, with some 27-32B models fitting at lower quantization. On Qwen 3.6 27B, it achieves approximately 38.6 tokens per second at Q3_K_M quantization. An additional 9 models fit with CPU offload — slower but usable.

NVIDIA's CUDA ecosystem provides broad out-of-the-box support across llama.cpp, Ollama, vLLM, and TensorRT-LLM. Among consumer GPUs, it sits above NVIDIA RTX 5060 and NVIDIA RTX 4500 Ada in performance, but below Intel Arc B580 12GB.

VendorNVIDIA
ArchitectureBlackwell
VRAM16 GB
Memory typeGDDR7
Memory bandwidth448 GB/s
Compute backendCUDA
TierConsumer
Released2025
Models (native)40 / 70
Models (offload)9 / 70
Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.

Popular models for this GPU

Qwen 3.5 35B-A3B (MoE)Yi 1.5 34B ChatQwen3 32BQwen 2.5 32B InstructQwen 2.5 Coder 32B Instruct

Models this GPU runs natively in VRAM (40)

Models that fit with CPU offload (9)

These use system RAM for layers that don't fit in VRAM — expect much slower inference.

Too large for this GPU (21)

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 40 of the 70 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 38.6 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 112 tokens per second.