Will It Run AI

Can Phi-4 Mini Reasoning 4B run on Intel Arc Pro A60 12GB?

YES — Runs Great

S88Excellent
Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~5.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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, 53.2 tok/s, Runs well
5.9 GB required12.0 GB available
49% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

83K

Memory

5.9 GB / 12.0 GB

Memory breakdown

Weights2.3 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on Intel Arc Pro A60 12GB
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

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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well53.2 tok/s1985 ms83K
CodingSRuns well53.2 tok/s3639 ms83K
Agentic CodingSRuns well53.2 tok/s5293 ms83K
ReasoningSRuns well53.2 tok/s4301 ms83K
RAGSRuns well53.2 tok/s6617 ms83K

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.5 GB
LowA85
Q3_K_S
3
1.9 GB
LowS85
NVFP4
4
2.1 GB
MediumS85
Q4_K_M
4
2.3 GB
MediumS86
Q5_K_M
5
2.7 GB
HighS86
Q6_K
6
3.1 GB
HighS87
Q8_0
8
4.1 GB
Very HighS88
F16Best for your GPU
16
7.8 GB
MaximumS89

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 Intel Arc Pro A60 12GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 9B9BS36.8 tok/s
AlibabaQwen 3 14B14BA14.9 tok/s
AlibabaQwen 3.5 4B4BS56 tok/s
AlibabaQwen 3 8B8BS41.4 tok/s
MicrosoftPhi-4-reasoning-plus 14B14.7BA12 tok/s

Frequently asked questions

Can Intel Arc Pro A60 12GB run Phi-4 Mini Reasoning 4B?

Yes, Intel Arc Pro A60 12GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 53.2 tok/s.

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

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.9 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 Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Phi-4 Mini Reasoning 4B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can Intel Arc Pro A60 12GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on Intel Arc Pro A60 12GB receives a S grade with 53.2 tok/s and 83K context.

What context window can Phi-4 Mini Reasoning 4B use on Intel Arc Pro A60 12GB?

On Intel Arc Pro A60 12GB, Phi-4 Mini Reasoning 4B can safely use up to 83K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Phi-4 Mini Reasoning 4B feels slow on Intel Arc Pro A60 12GB?

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.

Would CUDA be a better path than Intel Arc Pro A60 12GB for Phi-4 Mini Reasoning 4B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc Pro A60 12GBSee all hardware for Phi-4 Mini Reasoning 4B
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