Can Phi 4 Mini 4B run on RTX 2060 Super 8GB?

YES — Runs Great

A76Great

○Estimated from fit model

Phi 4 Mini 4B needs ~5.9 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 5.9 GB, 56.0 tok/s, Runs well

5.9 GB required8.0 GB available

74% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

39K

Memory

5.9 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsPhi 4 Mini 4B on RTX 2060 Super 8GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	56.0 tok/s	1886 ms	39K
Coding	A	Runs well	56.0 tok/s	3457 ms	39K
Agentic Coding	A	Tight fit	56.0 tok/s	5029 ms	39K
Reasoning	A	Runs well	56.0 tok/s	4086 ms	39K
RAG	A	Tight fit	56.0 tok/s	6286 ms	39K

Quantization options

How Phi 4 Mini 4B (4B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A72
Q3_K_S	3	2.0 GB	Low	A72
NVFP4	4	2.2 GB	Medium	A73
Q4_K_M	4	2.4 GB	Medium	A73
Q5_K_M	5	2.9 GB	High	A74
Q6_K	6	3.3 GB	High	A74
Q8_0Best for your GPU	8	4.3 GB	Very High	A74
F16	16	8.2 GB	Maximum	F0

Get started

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

Run

ollama run phi4-mini

Your hardware

More models your RTX 2060 Super 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 8B	8B	A	31.6 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	33.6 tok/s
👁 InternLM InternVL2 8B	8B	A	33.6 tok/s
👁 Mistral Ministral 3 8B	8B	A	31.6 tok/s
👁 OpenBMB MiniCPM-V 2.6 8B	8B	A	33.6 tok/s

Frequently asked questions

See all results for RTX 2060 Super 8GB See all hardware for Phi 4 Mini 4B

URL: https://willitrunai.com/can-run/phi-4-mini-4b-on-rtx-2060-super-8gb