What LLMs Can I Run on a GeForce RTX 4060 Ti 16GB?

16 GB of VRAM, of which about 14.7 GB is usable for a model. That runs 11 of the 20 models here , up to DeepSeek-R1 Distill Qwen 14B.

Largest model that fits: DeepSeek-R1 Distill Qwen 14B at Q6_K, needing 10.0 GB and leaving 4.7 GB spare. Its context can run to 33K tokens before memory runs out. Slow by gaming standards but carries 16 GB, which for holding a model matters more than the bandwidth does.

Every model, on this hardware

Which models run on a GeForce RTX 4060 Ti 16GB
Model	Size	Needs	Runs	Max context
Llama 3.2 1B	1.2B	1.7 GB	Yes	128K
Llama 3.2 3B	3.2B	3.4 GB	Yes	112K
Llama 3.1 8B	8.0B	6.0 GB	Yes	78K
Llama 3.3 70B	70.5B	40.3 GB	No	—
Qwen2.5 3B	3.1B	2.7 GB	Yes	32K
Qwen2.5 7B	7.6B	5.2 GB	Yes	32K
Qwen2.5 14B	14.8B	10.0 GB	Yes	32K
Qwen2.5 32B	32.8B	20.0 GB	No	—
Qwen2.5 72B	72.7B	41.4 GB	No	—
Qwen2.5 Coder 7B	7.6B	5.2 GB	Yes	32K
Qwen2.5 Coder 32B	32.8B	20.0 GB	No	—
Qwen3 32B	32.8B	20.0 GB	No	—
Mistral 7B v0.3	7.3B	5.6 GB	Yes	32K
Gemma 2 9B	9.2B	8.3 GB	Yes	8K
Gemma 2 27B	27.2B	17.9 GB	No	—
Phi-4	14.7B	10.0 GB	Yes	16K
Qwen3 30B-A3B	30.5B	17.5 GB	No	—
Qwen3 235B-A22B	235.1B	125.4 GB	No	—
Mixtral 8x7B	46.7B	26.3 GB	No	—
DeepSeek-R1 Distill Qwen 14B	14.8B	10.0 GB	Yes	33K

Q4_K_M at 8K context. "Max context" is the longest context that still fits once the weights are loaded.

Memory decides this, not speed

For inference, memory capacity decides what runs and bandwidth decides how fast. A slower card with more memory beats a faster card with less, every time, because a model that does not fit does not run at any speed. That is why a 16 GB RTX 4060 Ti is a more useful local-LLM card than a faster 12 GB one.

The other half is context. Weights are a fixed cost, but the key-value cache grows with every token, and on a long conversation it can outgrow the weights. That is why the table above gives a maximum context per model rather than a yes or no — plenty of models load fine on this hardware and then run out of room mid-conversation.

Frequently asked questions

What LLMs can a GeForce RTX 4060 Ti 16GB run?

11 of the 20 models on this site, at Q4_K_M with a 8K context. The largest is DeepSeek-R1 Distill Qwen 14B at 14.8B parameters, needing 10.0 GB of the roughly 14.7 GB usable.

Why is usable memory less than the 16 GB on the box?

The framework, the driver and, on a card driving a display, the screen itself all take a share before the weights get any. This page assumes about 14.7 GB of the 16 GB is available, which is why a model that looks like it fits on paper sometimes does not.

Does a bigger model at a rougher quantisation beat a smaller one?

Usually, yes. A 32B model at Q3_K_M generally outperforms a 7B at Q8_0, even though the smaller model is barely quantised at all. The practical rule is to fit the largest model you can and then step the quantisation down only as far as you must — which is what the "best format" column above is showing.

How accurate are these figures?

They count weights, the key-value cache at the chosen context, and a fixed runtime allowance. Real usage moves with the runtime, the batch size and whether flash attention is enabled. Treat anything within a gigabyte of capacity as "probably not" rather than "just fits" — which is why the table says "only just" rather than "yes" past 95% of usable memory.

Manufacturer specifications for VRAM and unified memory. Model config.json files published on Hugging Face.

An estimate of memory, not a benchmark. It counts model weights, the key-value cache at the context you choose, and a fixed runtime allowance. Actual usage moves with the runtime, the batch size, whether flash attention is on, and how much the operating system has already taken from a shared memory pool. Treat a result within a gigabyte of your card's capacity as 'probably not' rather than 'just fits'.

Data on this page last verified 2 September 2026.