# Can I run Qwen3-8B on an RTX 3070 Ti? | What Can I Run AI?

> Yes. Qwen3-8B at Q4_K_M needs about 6.7 GB; RTX 3070 Ti has 8.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3070-ti/

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**Yes.** Qwen3-8B at Q4\_K\_M needs about 6.7 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable at a \~4k context.

# Can I run Qwen3-8B on an RTX 3070 Ti?

Qwen3-8B at Q4\_K\_M needs about 6.7 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable. That figure is 8 GB of VRAM on the spec sheet, of which 8.0 GB is usable for weights and cache, at 608 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

Q4\_K\_M Fits with headroom

Memory needed

6.7 GB at Q4\_K\_M, \~4k context

Usable VRAM

8.0 GB of 8 GB VRAM

Est. tokens/sec

73–104 estimate

[Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/) · [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3070-ti/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3070-ti/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 8.0 GB | Verdict       |
| -------- | -------------- | ------------- |
| Q4\_K\_M | 6.7 GB         | Q4Fits        |
| Q8\_0    | 11.0 GB        | Q8Won’t fit   |
| FP16     | 19.8 GB        | FP16Won’t fit |

## Estimated tokens per second

Estimate: **73–104 tokens/sec** at Q4\_K\_M. 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 Q4\_K\_M, this machine has headroom out to about **8k 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 3070 Ti at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.6 GB          | 6.7 GB            | Fits      |
| 8k      | 1.2 GB          | 7.3 GB            | Tight     |
| 16k     | 2.4 GB          | 8.5 GB            | Won’t fit |
| 32k     | 4.8 GB          | 10.9 GB           | Won’t fit |
| 64k     | 9.7 GB          | 15.8 GB           | Won’t fit |
| 128k    | 19.3 GB         | 25.4 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

* Q8\_0 needs 11.0 GB and does not fit here. The [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/hardware/rtx-3060-12gb/) has 12.0 GB usable, enough for Q8.
* 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 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3080-10gb/)Fits
* [Qwen3-8B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-4060-ti-16gb/)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 3070 Ti

* [Qwen3-32B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3070-ti/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3070-ti/)Won’t fit
* [Qwen3.8-27B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3070-ti/)Won’t fit
* [Gemma 3 27B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3070-ti/)Won’t fit
* [Gemma 4 31B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3070-ti/)Won’t fit
* [gpt-oss-20b on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3070-ti/)Won’t fit

## Quick answers

Can I run Qwen3-8B on an RTX 3070 Ti?

Yes. Qwen3-8B at Q4\_K\_M needs about 6.7 GB, and the GeForce RTX 3070 Ti has 8.0 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 3070 Ti?

Our estimate is 73–104 tokens per second at Q4\_K\_M. It is not a measured benchmark.

What context length can I use?

At Q4\_K\_M, our KV estimate supports up to about 8k tokens before the cache pushes the total over usable memory.

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