# Can I run Llama 3.2 3B on an RTX 4060 Ti 16GB?

> Yes. Llama 3.2 3B at FP16 needs about 8.4 GB; RTX 4060 Ti 16GB has 16.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-4060-ti-16gb/

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**Yes.** Llama 3.2 3B at FP16 needs about 8.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a \~4k context.

# Can I run Llama 3.2 3B on an RTX 4060 Ti 16GB?

Llama 3.2 3B at FP16 needs about 8.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 288 GB/s memory bandwidth. A tiny Meta Llama, aimed at phones and low-VRAM cards. Quality is limited; it is the model you pick when memory is tight. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

FP16 Fits with headroom

Memory needed

8.4 GB at FP16, \~4k context

Usable VRAM

16.0 GB of 16 GB VRAM

Est. tokens/sec

27–38 estimate

[Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/) · [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/hardware/rtx-4060-ti-16gb/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4060-ti-16gb/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 16.0 GB | Verdict  |
| -------- | --------------- | -------- |
| Q4\_K\_M | 3.2 GB          | Q4Fits   |
| Q8\_0    | 4.9 GB          | Q8Fits   |
| FP16     | 8.4 GB          | FP16Fits |

## Estimated tokens per second

Estimate: **27–38 tokens/sec** at FP16\. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from [How we calculate](https://whatcanirunai.vercel.app/method/).

## Context headroom

At FP16, this machine has headroom out to about **64k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Llama 3.2 3B on an RTX 4060 Ti 16GB at FP16__
| Context | KV cache (FP16) | Total at FP16 | Verdict   |
| ------- | --------------- | ------------- | --------- |
| 4k      | 0.5 GB          | 8.4 GB        | Fits      |
| 8k      | 0.9 GB          | 8.9 GB        | Fits      |
| 16k     | 1.9 GB          | 9.8 GB        | Fits      |
| 32k     | 3.8 GB          | 11.7 GB       | Fits      |
| 64k     | 7.5 GB          | 15.4 GB       | Tight     |
| 128k    | 15.0 GB         | 23.0 GB       | Won’t fit |

From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/).

## What to do instead

* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/llama/) and the [Llama-3.2-3B-Instruct model page](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/).

## Other hardware for Llama 3.2 3B

* [Llama 3.2 3B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-3060-12gb/)Fits
* [Llama 3.2 3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-3070-ti/)Fits
* [Llama 3.2 3B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-4090/)Fits
* [Llama 3.2 3B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/arc-b580/)Fits
* [Llama 3.2 3B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/mac-mini-m6-16/)Fits

## Other models on an RTX 4060 Ti 16GB

* [Qwen3-32B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4060-ti-16gb/)Won’t fit
* [Qwen3-30B-A3B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4060-ti-16gb/)Won’t fit
* [Qwen3.8-27B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-4060-ti-16gb/)Won’t fit
* [Gemma 3 27B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-4060-ti-16gb/)Won’t fit
* [Gemma 4 31B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4060-ti-16gb/)Won’t fit
* [gpt-oss-20b on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/)Tight

## Quick answers

Can I run Llama 3.2 3B on an RTX 4060 Ti 16GB?

Yes. Llama 3.2 3B at FP16 needs about 8.4 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable.

How much VRAM does Llama 3.2 3B need?

About 3.2 GB at Q4\_K\_M, 4.9 GB at Q8\_0 and 8.4 GB at FP16, estimated for a \~4k context.

How fast is Llama 3.2 3B on an RTX 4060 Ti 16GB?

Our estimate is 27–38 tokens per second at FP16\. It is not a measured benchmark.

What context length can I use?

At FP16, our KV estimate supports up to about 64k tokens before the cache pushes the total over usable memory.

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