Will It Run AI

Can Phi-4-reasoning-plus 14B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S86Excellent
Estimated from fit model

Phi-4-reasoning-plus 14B needs ~25.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~206 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 25.7 GB, 205.8 tok/s, Runs well
25.7 GB required128.0 GB available
20% VRAM used

Fit status

Runs well

Decode

205.8 tok/s

TTFT

941 ms

Safe context

33K

Memory

25.7 GB / 128.0 GB

Memory breakdown

Weights9.0 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsPhi-4-reasoning-plus 14B on Intel Data Center GPU Max 1550 128GB
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: 205.8 tok/s decode · 941ms TTFT (warm) · 515 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 well205.8 tok/s513 ms33K
CodingSRuns well205.8 tok/s941 ms33K
Agentic CodingSRuns well205.8 tok/s1368 ms33K
ReasoningSRuns well205.8 tok/s1112 ms33K
RAGSRuns well205.8 tok/s1710 ms33K

Quantization options

How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.7 GB
LowA78
Q3_K_S
3
7.2 GB
LowA78
NVFP4
4
8.2 GB
MediumA78
Q4_K_M
4
9.0 GB
MediumA78
Q5_K_M
5
10.6 GB
HighA78
Q6_K
6
12.1 GB
HighA78
Q8_0
8
15.7 GB
Very HighA78
F16Best for your GPU
16
30.1 GB
MaximumA80

Get started

Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.

Run

ollama run phi4-reasoning

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS29.2 tok/s
AlibabaQwen3-Coder 30B A3B Instruct30.5BS304.8 tok/s
AlibabaQwen 3.5 27B27BS132.2 tok/s
AlibabaQwen 3.6 27B27BS82.4 tok/s
AlibabaQwen 3.5 122B A10B122BS81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Phi-4-reasoning-plus 14B?

Yes, Intel Data Center GPU Max 1550 128GB can run Phi-4-reasoning-plus 14B with a S grade (Runs well). Expected decode speed: 205.8 tok/s.

How much VRAM does Phi-4-reasoning-plus 14B need?

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 25.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4-reasoning-plus 14B?

The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4-reasoning-plus 14B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Phi-4-reasoning-plus 14B achieves approximately 205.8 tokens per second decode speed with a time-to-first-token of 941ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on Intel Data Center GPU Max 1550 128GB receives a S grade with 205.8 tok/s and 33K context.

What context window can Phi-4-reasoning-plus 14B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Phi-4-reasoning-plus 14B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Phi-4-reasoning-plus 14B feels slow on Intel Data Center GPU Max 1550 128GB?

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 Data Center GPU Max 1550 128GB for Phi-4-reasoning-plus 14B?

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.

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