How much VRAM does the NVIDIA RTX 5060 have?

The NVIDIA RTX 5060 has 8 GB of GDDR7 with 448 GB/s memory bandwidth.

What LLMs can the NVIDIA RTX 5060 run locally?

The NVIDIA RTX 5060 can run 24 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 NVFP4, Llama 3.2 1B Instruct at FP32.

Can the NVIDIA RTX 5060 run Llama 3.3 70B Instruct?

The NVIDIA RTX 5060 can run Llama 3.3 70B Instruct with CPU offload at Q2_K quantization, but inference will be slower than native VRAM execution.

Can the NVIDIA RTX 5060 run Qwen 3.6 27B?

The NVIDIA RTX 5060 can run Qwen 3.6 27B with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.

Can the NVIDIA RTX 5060 run Llama 3.1 8B Instruct?

Yes. The NVIDIA RTX 5060 runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 112 tokens per second.

NVIDIA RTX 5060

The NVIDIA RTX 5060 has 8 GB VRAM and 448 GB/s memory bandwidth. It can run 24 of our 70 tracked models natively in VRAM at 8k context.

The NVIDIA RTX 5060 is the entry-level Blackwell GPU with 8GB GDDR7 on a 128-bit bus (448 GB/s) and 3,840 CUDA cores. It is strictly a 1080p gaming card; for LLM inference, only small models like Gemma 4 E4B or Phi-3 Mini fit comfortably in VRAM.

The NVIDIA RTX 5060 is a consumer-grade NVIDIA GPU based on the Blackwell architecture. Released in 2025. It features 8 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 24 of the 70 models we track natively in VRAM at 8K context. The largest model it handles in VRAM is Qwen3 14B (92 t/s at Q2_K). Its VRAM limits it to smaller models (1-8B parameters), which makes it suitable for prototyping and edge inference. On Llama 3.1 8B Instruct, it achieves approximately 112 tokens per second at NVFP4 quantization. An additional 23 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 5050 and NVIDIA RTX 3060 12GB in performance, but below NVIDIA RTX 5060 Ti 16GB.

Vendor	NVIDIA
Architecture	Blackwell
VRAM	8 GB
Memory type	GDDR7
Memory bandwidth	448 GB/s
Compute backend	CUDA
Tier	Consumer
Released	2025
Models (native)	24 / 70
Models (offload)	23 / 70

Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.

Models this GPU runs natively in VRAM (24)

Models that fit with CPU offload (23)

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

Too large for this GPU (23)

Frequently asked questions

How much VRAM does the NVIDIA RTX 5060 have?: The NVIDIA RTX 5060 has 8 GB of GDDR7 with 448 GB/s memory bandwidth.
What LLMs can the NVIDIA RTX 5060 run locally?: The NVIDIA RTX 5060 can run 24 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 NVFP4, Llama 3.2 1B Instruct at FP32.
Can the NVIDIA RTX 5060 run Llama 3.3 70B Instruct?: The NVIDIA RTX 5060 can run Llama 3.3 70B Instruct with CPU offload at Q2_K quantization, but inference will be slower than native VRAM execution.
Can the NVIDIA RTX 5060 run Qwen 3.6 27B?: The NVIDIA RTX 5060 can run Qwen 3.6 27B with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.
Can the NVIDIA RTX 5060 run Llama 3.1 8B Instruct?: Yes. The NVIDIA RTX 5060 runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 112 tokens per second.