# Can I run Phi-4 on an RTX 4070 Ti Super? | What Can I Run AI

> Yes. Phi-4 at Q4_K_M needs about 11.2 GB; RTX 4070 Ti Super has 16.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/phi-4/rtx-4070-ti-super/

[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 4070 Ti Super

**Yes.** Phi-4 at Q4\_K\_M needs about 11.2 GB, and the GeForce RTX 4070 Ti Super has 16.0 GB usable at a \~4k context.

# Can I run Phi-4 on an RTX 4070 Ti Super?

Phi-4 at Q4\_K\_M needs about 11.2 GB, and the GeForce RTX 4070 Ti Super has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 672 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

Q4\_K\_M Fits with headroom

Memory needed

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

Usable VRAM

16.0 GB of 16 GB VRAM

Est. tokens/sec

45–64 estimate

[phi-4](https://whatcanirunai.vercel.app/models/phi-4/) · [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti-super/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4070-ti-super/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 16.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 11.2 GB         | Q4Fits        |
| Q8\_0    | 18.9 GB         | Q8Won’t fit   |
| FP16     | 34.7 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **45–64 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 **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 4070 Ti Super at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.8 GB          | 11.2 GB           | Fits      |
| 8k      | 1.7 GB          | 12.1 GB           | Fits      |
| 16k     | 3.4 GB          | 13.7 GB           | Fits      |
| 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

* Q8\_0 needs 18.9 GB and does not fit here. The [Radeon RX 7900 XT](https://whatcanirunai.vercel.app/hardware/rx-7900-xt/) has 20.0 GB usable, enough for Q8.
* Full hardware list: [phi-4](https://whatcanirunai.vercel.app/models/phi-4/). Specs for this machine: [GeForce RTX 4070 Ti Super](https://whatcanirunai.vercel.app/hardware/rtx-4070-ti-super/).

## 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 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 4070 Ti Super

* [Qwen3-32B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4070-ti-super/)Won’t fit
* [Qwen3-30B-A3B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4070-ti-super/)Won’t fit
* [Qwen3.8-27B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-4070-ti-super/)Won’t fit
* [Gemma 3 27B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-4070-ti-super/)Won’t fit
* [Gemma 4 31B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4070-ti-super/)Won’t fit
* [gpt-oss-20b on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4070-ti-super/)Tight

## Quick answers

Can I run Phi-4 on an RTX 4070 Ti Super?

Yes. Phi-4 at Q4\_K\_M needs about 11.2 GB, and the GeForce RTX 4070 Ti Super has 16.0 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 4070 Ti Super?

Our estimate is 45–64 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 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/