# Can I run Qwen3-8B on an RX 7900 XTX? | What Can I Run AI?

> Yes. Qwen3-8B at FP16 needs about 19.8 GB; RX 7900 XTX has 24.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rx-7900-xtx/

[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 RX 7900 XTX

**Yes.** Qwen3-8B at FP16 needs about 19.8 GB, and the Radeon RX 7900 XTX has 24.0 GB usable at a \~4k context.

# Can I run Qwen3-8B on an RX 7900 XTX?

Qwen3-8B at FP16 needs about 19.8 GB, and the Radeon RX 7900 XTX has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 960 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

FP16 Fits with headroom

Memory needed

19.8 GB at FP16, \~4k context

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

35–50 estimate

[Qwen3-8B](https://whatcanirunai.vercel.app/models/qwen3-8b/) · [Radeon RX 7900 XTX](https://whatcanirunai.vercel.app/hardware/rx-7900-xtx/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rx-7900-xtx/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 24.0 GB | Verdict  |
| -------- | --------------- | -------- |
| Q4\_K\_M | 6.7 GB          | Q4Fits   |
| Q8\_0    | 11.0 GB         | Q8Fits   |
| FP16     | 19.8 GB         | FP16Fits |

## Estimated tokens per second

Estimate: **35–50 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 **16k 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 RX 7900 XTX at FP16__
| Context | KV cache (FP16) | Total at FP16 | Verdict   |
| ------- | --------------- | ------------- | --------- |
| 4k      | 0.6 GB          | 19.8 GB       | Fits      |
| 8k      | 1.2 GB          | 20.4 GB       | Fits      |
| 16k     | 2.4 GB          | 21.7 GB       | Tight     |
| 32k     | 4.8 GB          | 24.1 GB       | Won’t fit |
| 64k     | 9.7 GB          | 28.9 GB       | Won’t fit |
| 128k    | 19.3 GB         | 38.6 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 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

## Other models on an RX 7900 XTX

* [Qwen3-32B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rx-7900-xtx/)Tight
* [Qwen3-30B-A3B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rx-7900-xtx/)Tight
* [Qwen3.8-27B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rx-7900-xtx/)Fits
* [Gemma 3 27B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rx-7900-xtx/)Fits
* [Gemma 4 31B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rx-7900-xtx/)Tight
* [gpt-oss-20b on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rx-7900-xtx/)Fits

## Quick answers

Can I run Qwen3-8B on an RX 7900 XTX?

Yes. Qwen3-8B at FP16 needs about 19.8 GB, and the Radeon RX 7900 XTX has 24.0 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 RX 7900 XTX?

Our estimate is 35–50 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 16k 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/