Can Phi 4 Mini 4B run on Intel Arc Pro A40 6GB?

YES — Tight Fit

A72Great

○Estimated from fit model

Phi 4 Mini 4B needs ~5.4 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~41 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) — 5.4 GB, 41.4 tok/s, Tight fit

5.4 GB required6.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

41.4 tok/s

TTFT

4671 ms

Safe context

23K

Memory

5.4 GB / 6.0 GB

Memory breakdown

Weights2.4 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsPhi 4 Mini 4B on Intel Arc Pro A40 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: 41.4 tok/s decode · 4.7s TTFT (warm) · 104 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	41.4 tok/s	2548 ms	23K
Coding	A	Tight fit	41.4 tok/s	4671 ms	23K
Agentic Coding	B	Very compromised (needs ~0.3 GB host RAM)	23.4 tok/s	12045 ms	23K
Reasoning	A	Tight fit	41.4 tok/s	5520 ms	23K
RAG	B	Very compromised (needs ~0.3 GB host RAM)	23.4 tok/s	15056 ms	23K

Quantization options

How Phi 4 Mini 4B (4B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A75
Q3_K_S	3	2.0 GB	Low	A75
NVFP4	4	2.2 GB	Medium	A75
Q4_K_M	4	2.4 GB	Medium	A75
Q5_K_M	5	2.9 GB	High	A75
Q6_KBest for your GPU	6	3.3 GB	High	A74
Q8_0	8	4.3 GB	Very High	F0
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 Intel Arc Pro A40 6GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 2.5 VL 7B	7B	B	14.6 tok/s
👁 Alibaba Qwen 2.5 7B	7B	B	14.6 tok/s
👁 Mistral AI Codestral Mamba 7B	7B	A	17.4 tok/s
👁 Google Gemma 4 E2B	5.1B	A	24.9 tok/s

Frequently asked questions

See all results for Intel Arc Pro A40 6GB See all hardware for Phi 4 Mini 4B

URL: https://willitrunai.com/can-run/phi-4-mini-4b-on-arc-pro-a40-6gb

⇱ Phi 4 Mini 4B on Intel Arc Pro A40 6GB? TIGHT FIT