Can the NVIDIA RTX 4060 run Llama 3.1 8B?

Yes — the 4-bit build is a 4.9 GB download and fits in 8 GB VRAM. Expect roughly ~48 tok/s.

What is the biggest LLM the NVIDIA RTX 4060 can run?

Gemma 3 12B is the largest model in our catalog that fits (7.4 GB at 4-bit). Expect about ~31 tok/s.

How fast is the NVIDIA RTX 4060 for local LLMs?

Token generation is memory-bandwidth bound. At roughly 272 GB/s, the NVIDIA RTX 4060 generates about ~48 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 4060 run?

The NVIDIA RTX 4060 has 8 GB VRAM and roughly 272 GB/s of memory bandwidth. Below is every model in our catalog that fits, with estimated generation speed. Biggest pick: Gemma 3 12B at ~31 tok/s.

Specifications

Memory8 GB VRAM

Bandwidth~272 GB/s

Memory typeDedicated VRAM

Released2023-06

Models on the NVIDIA RTX 4060

32 / 73 models

Model	Download (Q4)	Fits?	~Speed
Gemma 3 12BGoogle	7.4 GB	Runs	~31 tok/s
Mistral Nemo 12BMistral AI	7.4 GB	Runs	~31 tok/s
Gemma 4 12BGoogle	7.3 GB	Runs	~32 tok/s
Mellum 2 12B-A2.5BJetBrains	7.3 GB	Runs	~153 tok/s
Qwen 3.5 9BAlibaba	5.5 GB	Runs	~42 tok/s
GLM-4.6V-FlashZ.ai	5.5 GB	Runs	~42 tok/s
Qwen 2.5 VL 7BAlibaba	5.0 GB	Runs	~46 tok/s
Qwen 3 8BAlibaba	5.0 GB	Runs	~47 tok/s
Granite 3.3 8BIBM	5.0 GB	Runs	~47 tok/s
Llama 3.1 8BMeta	4.9 GB	Runs	~48 tok/s
DeepSeek R1 8BDeepSeek	4.9 GB	Runs	~48 tok/s
Gemma 4 E4BGoogle	4.9 GB	Runs	~85 tok/s
Qwen3-VL 8BAlibaba	4.9 GB	Runs	~48 tok/s
Ministral 3 8BMistral AI	4.9 GB	Runs	~48 tok/s
Gemma 3n E4BGoogle	4.7 GB	Runs	~95 tok/s
Qwen 2.5 Coder 7BAlibaba	4.6 GB	Runs	~50 tok/s
DeepSeek R1 7BDeepSeek	4.6 GB	Runs	~50 tok/s
Mistral 7BMistral AI	4.4 GB	Runs	~53 tok/s
Gemma 4 E2BGoogle	3.1 GB	Runs	~166 tok/s
Gemma 3 4BGoogle	2.6 GB	Runs	~89 tok/s
Qwen 3 4BAlibaba	2.4 GB	Runs	~95 tok/s
Qwen 3.5 4BAlibaba	2.4 GB	Runs	~95 tok/s
Phi-4 Mini 3.8BMicrosoft	2.3 GB	Runs	~100 tok/s
Llama 3.2 3BMeta	1.9 GB	Runs	~119 tok/s
DeepSeek-OCRDeepSeek	1.8 GB	Runs	~669 tok/s
Ministral 3 3BMistral AI	1.8 GB	Runs	~127 tok/s
DeepSeek R1 1.5BDeepSeek	1.1 GB	Runs	~212 tok/s
Qwen 3 1.7BAlibaba	1.0 GB	Runs	~224 tok/s
SmolLM2 1.7BHugging Face	1.0 GB	Runs	~224 tok/s
Llama 3.2 1BMeta	0.7 GB	Runs	~318 tok/s
Gemma 3 1BGoogle	0.6 GB	Runs	~381 tok/s
Qwen 3 0.6BAlibaba	0.4 GB	Runs	~636 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 →