Can the NVIDIA RTX 3060 run Llama 3.1 8B?

Yes — the 4-bit build is a 4.9 GB download and fits in 12 GB VRAM. Expect roughly ~63 tok/s.

What is the biggest LLM the NVIDIA RTX 3060 can run?

Phi-4 Reasoning Vision 15B is the largest model in our catalog that fits (9.1 GB at 4-bit). Expect about ~34 tok/s.

How fast is the NVIDIA RTX 3060 for local LLMs?

Token generation is memory-bandwidth bound. At roughly 360 GB/s, the NVIDIA RTX 3060 generates about ~63 tok/s on an 8B-class model at 4-bit — speed scales inversely with model size.

Does the whole model need to fit in VRAM?

For full GPU speed, yes. Runtimes like llama.cpp can split layers between VRAM and system RAM, but every layer that spills to RAM slows generation down sharply.

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What LLMs can the NVIDIA RTX 3060 run?

The NVIDIA RTX 3060 has 12 GB VRAM and roughly 360 GB/s of memory bandwidth. Below is every model in our catalog that fits, with estimated generation speed. Biggest pick: Phi-4 Reasoning Vision 15B at ~34 tok/s.

Specifications

Memory12 GB VRAM

Bandwidth~360 GB/s

Memory typeDedicated VRAM

Released2021-02

Models on the NVIDIA RTX 3060

38 / 73 models

Model	Download (Q4)	Fits?	~Speed
Phi-4 Reasoning Vision 15BMicrosoft	9.1 GB	Runs	~34 tok/s
Qwen 3 14BAlibaba	9.0 GB	Runs	~34 tok/s
DeepSeek R1 14BDeepSeek	9.0 GB	Runs	~34 tok/s
Phi-4 14BMicrosoft	8.9 GB	Runs	~34 tok/s
Ministral 3 14BMistral AI	8.5 GB	Runs	~36 tok/s
OLMo 2 13BAi2	8.3 GB	Runs	~37 tok/s
Gemma 3 12BGoogle	7.4 GB	Runs	~41 tok/s
Mistral Nemo 12BMistral AI	7.4 GB	Runs	~41 tok/s
Gemma 4 12BGoogle	7.3 GB	Runs	~42 tok/s
Mellum 2 12B-A2.5BJetBrains	7.3 GB	Runs	~202 tok/s
Qwen 3.5 9BAlibaba	5.5 GB	Runs	~56 tok/s
GLM-4.6V-FlashZ.ai	5.5 GB	Runs	~56 tok/s
Qwen 2.5 VL 7BAlibaba	5.0 GB	Runs	~61 tok/s
Qwen 3 8BAlibaba	5.0 GB	Runs	~62 tok/s
Granite 3.3 8BIBM	5.0 GB	Runs	~62 tok/s
Llama 3.1 8BMeta	4.9 GB	Runs	~63 tok/s
DeepSeek R1 8BDeepSeek	4.9 GB	Runs	~63 tok/s
Gemma 4 E4BGoogle	4.9 GB	Runs	~112 tok/s
Qwen3-VL 8BAlibaba	4.9 GB	Runs	~63 tok/s
Ministral 3 8BMistral AI	4.9 GB	Runs	~63 tok/s
Gemma 3n E4BGoogle	4.7 GB	Runs	~126 tok/s
Qwen 2.5 Coder 7BAlibaba	4.6 GB	Runs	~66 tok/s
DeepSeek R1 7BDeepSeek	4.6 GB	Runs	~66 tok/s
Mistral 7BMistral AI	4.4 GB	Runs	~70 tok/s
Gemma 4 E2BGoogle	3.1 GB	Runs	~219 tok/s
Gemma 3 4BGoogle	2.6 GB	Runs	~117 tok/s
Qwen 3 4BAlibaba	2.4 GB	Runs	~126 tok/s
Qwen 3.5 4BAlibaba	2.4 GB	Runs	~126 tok/s
Phi-4 Mini 3.8BMicrosoft	2.3 GB	Runs	~133 tok/s
Llama 3.2 3BMeta	1.9 GB	Runs	~158 tok/s
DeepSeek-OCRDeepSeek	1.8 GB	Runs	~886 tok/s
Ministral 3 3BMistral AI	1.8 GB	Runs	~168 tok/s
DeepSeek R1 1.5BDeepSeek	1.1 GB	Runs	~280 tok/s
Qwen 3 1.7BAlibaba	1.0 GB	Runs	~297 tok/s
SmolLM2 1.7BHugging Face	1.0 GB	Runs	~297 tok/s
Llama 3.2 1BMeta	0.7 GB	Runs	~421 tok/s
Gemma 3 1BGoogle	0.6 GB	Runs	~505 tok/s
Qwen 3 0.6BAlibaba	0.4 GB	Runs	~841 tok/s

To run fully on the GPU, the 4-bit build must fit in VRAM. Models that don't fit can still run on CPU + system RAM, several times slower. · Data updated: 2026-06-11 · How we calculate these numbers →