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

YES — Runs Great

A84Great

Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~7.9 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~72 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 7.9 GB, 72.2 tok/s, Runs well

7.9 GB required32.0 GB available

25% VRAM used

Fit status

Runs well

Decode

72.2 tok/s

TTFT

2681 ms

Safe context

131K

Memory

7.9 GB / 32.0 GB

Memory breakdown

Weights2.3 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B 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: 72.2 tok/s decode · 2.7s TTFT (warm) · 181 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	72.2 tok/s	1463 ms	131K
Coding	A	Runs well	72.2 tok/s	2681 ms	131K
Agentic Coding	A	Runs well	72.2 tok/s	3900 ms	131K
Reasoning	A	Runs well	72.2 tok/s	3169 ms	131K
RAG	A	Runs well	72.2 tok/s	4875 ms	131K

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	A80
Q3_K_S	3	1.9 GB	Low	A80
NVFP4	4	2.1 GB	Medium	A80
Q4_K_M	4	2.3 GB	Medium	A80
Q5_K_M	5	2.7 GB	High	A80
Q6_K	6	3.1 GB	High	A80
Q8_0	8	4.1 GB	Very High	A80
F16Best for your GPU	16	7.8 GB	Maximum	A82

Get started

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

Run

ollama run phi4-mini

Your hardware

More models your RTX 5090 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	130.7 tok/s
Qwen 3.5 27B	27B	S	58.5 tok/s
Qwen 3.6 27B	27B	S	35.1 tok/s
Qwen 3.6 35B A3B	35B	S	128.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	187.8 tok/s

Frequently asked questions

Can RTX 5090 32GB run Phi-4 Mini Reasoning 4B?

Yes, RTX 5090 32GB can run Phi-4 Mini Reasoning 4B with a A grade (Runs well). Expected decode speed: 72.2 tok/s.

How much VRAM does Phi-4 Mini Reasoning 4B need?

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4 Mini Reasoning 4B?

The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4 Mini Reasoning 4B run at on RTX 5090 32GB?

On RTX 5090 32GB, Phi-4 Mini Reasoning 4B achieves approximately 72.2 tokens per second decode speed with a time-to-first-token of 2681ms using Q4_K_M quantization.

Can RTX 5090 32GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on RTX 5090 32GB receives a A grade with 72.2 tok/s and 131K context.

What context window can Phi-4 Mini Reasoning 4B use on RTX 5090 32GB?

On RTX 5090 32GB, Phi-4 Mini Reasoning 4B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

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

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