# Can I run Qwen3-8B on an RTX 4060 Ti 16GB? | What Can I Run?

> Yes. Qwen3-8B at Q8_0 needs about 11.0 GB; RTX 4060 Ti 16GB has 16.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-4060-ti-16gb/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/) / Qwen3-8B on an RTX 4060 Ti 16GB

**Yes.** Qwen3-8B at Q8\_0 needs about 11.0 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a \~4k context.

# Can I run Qwen3-8B on an RTX 4060 Ti 16GB?

Qwen3-8B at Q8\_0 needs about 11.0 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. Alibaba’s 8B Qwen3\. A strong small dense model, and a common upgrade from older 7B–8B weights. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q8\_0 Fits with headroom

Memory needed

11.0 GB at Q8\_0, \~4k context

Usable VRAM

16.0 GB of 16 GB VRAM

Est. tokens/sec

20–28 estimate

[Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/) · [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 | 6.7 GB          | Q4Fits        |
| Q8\_0    | 11.0 GB         | Q8Fits        |
| FP16     | 19.8 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **20–28 tokens/sec** at Q8\_0\. 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 Q8\_0, this machine has headroom out to about **32k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Qwen3-8B on an RTX 4060 Ti 16GB at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict   |
| ------- | --------------- | -------------- | --------- |
| 4k      | 0.6 GB          | 11.0 GB        | Fits      |
| 8k      | 1.2 GB          | 11.6 GB        | Fits      |
| 16k     | 2.4 GB          | 12.8 GB        | Fits      |
| 32k     | 4.8 GB          | 15.2 GB        | Tight     |
| 64k     | 9.7 GB          | 20.1 GB        | Won’t fit |
| 128k    | 19.3 GB         | 29.7 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/qwen3/) and the [Qwen3-8B model page](https://whatcanirunai.vercel.app/models/qwen3-8b/).

## Other hardware for Qwen3-8B

* [Qwen3-8B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3060-12gb/)Tight
* [Qwen3-8B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3070-ti/)Fits
* [Qwen3-8B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3080-10gb/)Fits
* [Qwen3-8B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-4070-ti-super/)Fits
* [Qwen3-8B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-4090/)Fits
* [Qwen3-8B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-5070-ti/)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 Qwen3-8B on an RTX 4060 Ti 16GB?

Yes. Qwen3-8B at Q8\_0 needs about 11.0 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable.

How much VRAM does Qwen3-8B need?

About 6.7 GB at Q4\_K\_M, 11.0 GB at Q8\_0 and 19.8 GB at FP16, estimated for a \~4k context.

How fast is Qwen3-8B on an RTX 4060 Ti 16GB?

Our estimate is 20–28 tokens per second at Q8\_0\. It is not a measured benchmark.

What context length can I use?

At Q8\_0, our KV estimate supports up to about 32k tokens before the cache pushes the total over usable memory.

---
Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/