# Can I run Qwen3-Coder 30B on an RTX 3090? | What Can I Run?

> Yes, but it is tight. Qwen3-Coder 30B at Q4_K_M needs about 22.3 GB; RTX 3090 has 24.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-3090/

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**Yes, but it is tight.** Qwen3-Coder 30B at Q4\_K\_M needs about 22.3 GB, and the GeForce RTX 3090 has 24.0 GB usable at a \~4k context.

# Can I run Qwen3-Coder 30B on an RTX 3090?

Qwen3-Coder 30B at Q4\_K\_M needs about 22.3 GB, and the GeForce RTX 3090 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 936 GB/s memory bandwidth. The coding-tuned sibling of Qwen3-30B-A3B. Memory need matches the base 30B-A3B; the weights are trained for code. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q4\_K\_M Fits, short context only

Memory needed

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

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

up to 437 estimate

[Qwen3-Coder-30B-A3B-Instruct](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/) · [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 24.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 22.3 GB         | Q4Tight       |
| Q8\_0    | 38.3 GB         | Q8Won’t fit   |
| FP16     | 71.2 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **up to 437 tokens/sec** at Q4\_K\_M (upper bound: this is a Mixture-of-Experts, and only the active weights are counted). 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 **16k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Qwen3-Coder 30B on an RTX 3090 at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.4 GB          | 22.3 GB           | Tight     |
| 8k      | 0.8 GB          | 22.7 GB           | Tight     |
| 16k     | 1.6 GB          | 23.5 GB           | Tight     |
| 32k     | 3.2 GB          | 25.1 GB           | Won’t fit |
| 64k     | 6.4 GB          | 28.3 GB           | Won’t fit |
| 128k    | 12.9 GB         | 34.8 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

* Run [Qwen3.8-27B](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3090/) instead (20.4 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* Q8\_0 needs 38.3 GB and does not fit here. The [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) has 48.0 GB usable, enough for Q8.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/coding-models/) and the [Qwen3-Coder-30B-A3B-Instruct model page](https://whatcanirunai.vercel.app/models/qwen3-coder-30b-a3b-instruct/).

## Other hardware for Qwen3-Coder 30B

* [Qwen3-Coder 30B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-4090/)Tight
* [Qwen3-Coder 30B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-5070-ti/)Won’t fit
* [Qwen3-Coder 30B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/rtx-3060-12gb/)Won’t fit
* [Qwen3-Coder 30B on a Mac mini 32GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/mac-mini-m6-32/)Tight
* [Qwen3-Coder 30B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3-coder-30b-a3b-instruct/arc-b580/)Won’t fit

## Other models on an RTX 3090

* [Qwen3-32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/)Tight
* [Qwen3-30B-A3B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3090/)Tight
* [Qwen3.8-27B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3090/)Fits
* [Gemma 3 27B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3090/)Fits
* [Gemma 4 31B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3090/)Tight
* [gpt-oss-20b on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3090/)Fits

## Quick answers

Can I run Qwen3-Coder 30B on an RTX 3090?

Yes, but it is tight. Qwen3-Coder 30B at Q4\_K\_M needs about 22.3 GB, and the GeForce RTX 3090 has 24.0 GB usable.

How much VRAM does Qwen3-Coder 30B need?

About 22.3 GB at Q4\_K\_M, 38.3 GB at Q8\_0 and 71.2 GB at FP16, estimated for a \~4k context.

How fast is Qwen3-Coder 30B on an RTX 3090?

Our estimate is up to 437 tokens per second at Q4\_K\_M, as an upper bound for this MoE. It is not a measured benchmark.

What context length can I use?

At Q4\_K\_M, our KV estimate supports up to about 16k tokens before the cache pushes the total over usable memory.

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