# Can I run Phi-4 on an RTX 4090? | What Can I Run AI?

> Yes. Phi-4 at Q8_0 needs about 18.9 GB; RTX 4090 has 24.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-4090/

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**Yes.** Phi-4 at Q8\_0 needs about 18.9 GB, and the GeForce RTX 4090 has 24.0 GB usable at a \~4k context.

# Can I run Phi-4 on an RTX 4090?

Phi-4 at Q8\_0 needs about 18.9 GB, and the GeForce RTX 4090 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 1008 GB/s memory bandwidth. Microsoft’s Phi-4, a dense 14B-class model often compared with other 12B–14B instruct 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

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

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

39–55 estimate

[phi-4](https://whatcanirunai.vercel.app/models/phi-4/) · [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 24.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 11.2 GB         | Q4Fits        |
| Q8\_0    | 18.9 GB         | Q8Fits        |
| FP16     | 34.7 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **39–55 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 **16k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Phi-4 on an RTX 4090 at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict   |
| ------- | --------------- | -------------- | --------- |
| 4k      | 0.8 GB          | 18.9 GB        | Fits      |
| 8k      | 1.7 GB          | 19.8 GB        | Fits      |
| 16k     | 3.4 GB          | 21.4 GB        | Fits      |
| 32k     | 6.7 GB          | 24.8 GB        | Won’t fit |
| 64k     | 13.4 GB         | 31.5 GB        | Won’t fit |
| 128k    | 26.8 GB         | 44.9 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

* Full hardware list: [phi-4](https://whatcanirunai.vercel.app/models/phi-4/). Specs for this machine: [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/).

## Other hardware for Phi-4

* [Phi-4 on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-3060-12gb/)Tight
* [Phi-4 on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-3070-ti/)Won’t fit
* [Phi-4 on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-3080-10gb/)Won’t fit
* [Phi-4 on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-4060-ti-16gb/)Fits
* [Phi-4 on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-4070-ti-super/)Fits
* [Phi-4 on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-5070-ti/)Fits

## Other models on an RTX 4090

* [Qwen3-32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4090/)Tight
* [Qwen3-30B-A3B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4090/)Tight
* [Qwen3.8-27B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-4090/)Fits
* [Gemma 3 27B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-4090/)Fits
* [Gemma 4 31B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4090/)Tight
* [gpt-oss-20b on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4090/)Fits

## Quick answers

Can I run Phi-4 on an RTX 4090?

Yes. Phi-4 at Q8\_0 needs about 18.9 GB, and the GeForce RTX 4090 has 24.0 GB usable.

How much VRAM does Phi-4 need?

About 11.2 GB at Q4\_K\_M, 18.9 GB at Q8\_0 and 34.7 GB at FP16, estimated for a \~4k context.

How fast is Phi-4 on an RTX 4090?

Our estimate is 39–55 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 16k 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/