Can Phi-4 14B run on Intel Arc Pro B50 16GB?

YES — Tight Fit

A80Great

○Estimated from fit model

Phi-4 14B needs ~14.1 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 14.1 GB, 15.2 tok/s, Tight fit

14.1 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

15.2 tok/s

TTFT

12715 ms

Safe context

16K

Memory

14.1 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache3.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi-4 14B on Intel Arc Pro B50 16GB
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: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	15.2 tok/s	6935 ms	16K
Coding	A	Tight fit	15.2 tok/s	12715 ms	16K
Agentic Coding	B	Runs with offload (needs ~0.6 GB host RAM)	10.1 tok/s	27842 ms	16K
Reasoning	A	Tight fit	15.2 tok/s	15027 ms	16K
RAG	B	Runs with offload (needs ~0.6 GB host RAM)	10.1 tok/s	34803 ms	16K

Quantization options

How Phi-4 14B (14B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A81
Q3_K_S	3	6.9 GB	Low	A82
NVFP4	4	7.8 GB	Medium	A83
Q4_K_M	4	8.5 GB	Medium	A83
Q5_K_M	5	10.1 GB	High	A83
Q6_KBest for your GPU	6	11.5 GB	High	A82
Q8_0	8	15.0 GB	Very High	F0
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 Intel Arc Pro B50 16GB can run

Model	Params	Grade	Decode
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	14.5 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	14.4 tok/s
👁 Mistral Codestral 2 25.08	22B	B	5.3 tok/s
👁 Tsinghua/Zhipu CogVLM2 19B	19B	A	8 tok/s

Frequently asked questions

See all results for Intel Arc Pro B50 16GB See all hardware for Phi-4 14B

URL: https://willitrunai.com/can-run/phi-4-14b-on-arc-pro-b50-16gb

⇱ Phi-4 14B on Intel Arc Pro B50 16GB? TIGHT FIT