Can Phi-4 14B run on RTX 5090 32GB?

YES — Runs Great

A84Great

○Estimated from fit model

Phi-4 14B needs ~16.0 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~151 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.0 GB, 151.1 tok/s, Runs well

16.0 GB required32.0 GB available

50% VRAM used

Fit status

Runs well

Decode

151.1 tok/s

TTFT

1281 ms

Safe context

16K

Memory

16.0 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsPhi-4 14B on RTX 5090 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 151.1 tok/s decode · 1.3s TTFT (warm) · 378 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	151.1 tok/s	699 ms	16K
Coding	A	Runs well	151.1 tok/s	1281 ms	16K
Agentic Coding	S	Runs well	151.1 tok/s	1863 ms	16K
Reasoning	A	Runs well	151.1 tok/s	1514 ms	16K
RAG	S	Runs well	151.1 tok/s	2329 ms	16K

Quantization options

How Phi-4 14B (14B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A76
Q3_K_S	3	6.9 GB	Low	A76
NVFP4	4	7.8 GB	Medium	A76
Q4_K_M	4	8.5 GB	Medium	A77
Q5_K_M	5	10.1 GB	High	A77
Q6_K	6	11.5 GB	High	A78
Q8_0Best for your GPU	8	15.0 GB	Very High	A80
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Phi-4 14B on your machine.

Run

ollama run phi4

Your hardware

More models your RTX 5090 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	181.6 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	78.7 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	79 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	128.2 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	187.8 tok/s

Frequently asked questions

See all results for RTX 5090 32GB See all hardware for Phi-4 14B

URL: https://willitrunai.com/can-run/phi-4-14b-on-rtx-5090-32gb