Can Phi-4 Mini Reasoning 4B run on RTX 2060 6GB?

YES — Tight Fit

S89Excellent

○Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~5.6 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 5.6 GB, 53.2 tok/s, Tight fit

5.6 GB required6.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

21K

Memory

5.6 GB / 6.0 GB

Memory breakdown

Weights2.3 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on RTX 2060 6GB
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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	53.2 tok/s	1985 ms	21K
Coding	S	Tight fit	53.2 tok/s	3639 ms	21K
Agentic Coding	A	Very compromised	42.3 tok/s	6653 ms	21K
Reasoning	S	Tight fit	53.2 tok/s	4301 ms	21K
RAG	A	Very compromised	42.3 tok/s	8317 ms	21K

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.5 GB	Low	S91
Q3_K_S	3	1.9 GB	Low	S92
NVFP4	4

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 2060 6GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 4B	4B	A	52.7 tok/s

Frequently asked questions

See all results for RTX 2060 6GB See all hardware for Phi-4 Mini Reasoning 4B

URL: https://willitrunai.com/can-run/phi-4-mini-reasoning-on-rtx-2060-6gb

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