# Can I run Phi-4 on an RTX 3080? | What Can I Run AI?

> No. Phi-4 needs about 11.2 GB at Q4_K_M, and RTX 3080 has 10.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-3080-10gb/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Phi-4](https://whatcanirunai.vercel.app/models/phi-4/) / Phi-4 on an RTX 3080

**No.** Phi-4 at Q4\_K\_M needs about 11.2 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable at a \~4k context.

# Can I run Phi-4 on an RTX 3080?

Phi-4 at Q4\_K\_M needs about 11.2 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable. That figure is 10 GB of VRAM on the spec sheet, of which 10.0 GB is usable for weights and cache, at 760 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

None of Q4, Q8 or FP16 needs 11.2 GB

Memory needed

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

Usable VRAM

10.0 GB of 10 GB VRAM

Est. tokens/sec

n/a does not fit

[phi-4](https://whatcanirunai.vercel.app/models/phi-4/) · [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-10gb/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3080-10gb/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 10.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 11.2 GB         | Q4Won’t fit   |
| Q8\_0    | 18.9 GB         | Q8Won’t fit   |
| FP16     | 34.7 GB         | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: Phi-4 does not fit in the 10.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

## Context headroom

Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap.

__Context headroom for Phi-4 on an RTX 3080 at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.8 GB          | 11.2 GB           | Won’t fit |
| 8k      | 1.7 GB          | 12.1 GB           | Won’t fit |
| 16k     | 3.4 GB          | 13.7 GB           | Won’t fit |
| 32k     | 6.7 GB          | 17.1 GB           | Won’t fit |
| 64k     | 13.4 GB         | 23.8 GB           | Won’t fit |
| 128k    | 26.8 GB         | 37.2 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 [Gemma 3 12B](https://whatcanirunai.vercel.app/can-i-run/gemma-3-12b-it/rtx-3080-10gb/) instead (9.5 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold Phi-4 at Q4, you need about 11.2 GB. The [GeForce RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-3060-12gb/) has 12.0 GB usable ($329 list price).
* Our estimate is only 1.2 GB over this machine’s usable memory, so a tighter quant or a shorter context may work in practice. We still mark it as a no under [the published formula](https://whatcanirunai.vercel.app/method/).
* CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
* Full hardware list: [phi-4](https://whatcanirunai.vercel.app/models/phi-4/). Specs for this machine: [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-10gb/).

## 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 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 4090](https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-4090/)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 3080

* [Qwen3-32B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3080-10gb/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3080-10gb/)Won’t fit
* [Qwen3.8-27B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3080-10gb/)Won’t fit
* [Gemma 3 27B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3080-10gb/)Won’t fit
* [Gemma 4 31B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3080-10gb/)Won’t fit
* [gpt-oss-20b on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3080-10gb/)Won’t fit

## Quick answers

Can I run Phi-4 on an RTX 3080?

No. Phi-4 at Q4\_K\_M needs about 11.2 GB, and the GeForce RTX 3080 10GB has 10.0 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 3080?

We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.

What context length can I use?

The weights already do not fit, so longer context is not the limiting factor.

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