Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Phi-4 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.

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

Q: What should I upgrade first if Phi-4 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.

YES — Runs Great

A78Great

Estimated from fit model

Phi-4 14B needs ~25.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~196 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 25.3 GB, 196.0 tok/s, Runs well

25.3 GB required128.0 GB available

20% VRAM used

Fit status

Runs well

Decode

196.0 tok/s

TTFT

988 ms

Safe context

16K

Memory

25.3 GB / 128.0 GB

Memory breakdown

Weights8.5 GB

KV Cache3.1 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsPhi-4 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: 196.0 tok/s decode · 988ms TTFT (warm) · 490 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	196.0 tok/s	539 ms	16K
Coding	A	Runs well	196.0 tok/s	988 ms	16K
Agentic Coding	A	Runs well	196.0 tok/s	1437 ms	16K
Reasoning	A	Runs well	196.0 tok/s	1167 ms	16K
RAG	A	Runs well	196.0 tok/s	1796 ms	16K

Inference speed

Phi-4 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi-4 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.4	Fits

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B70
Q3_K_S	3	6.9 GB	Low	B70
NVFP4	4

Get started

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

Run

ollama run phi4

Your hardware

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

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

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

Yes, Intel Data Center GPU Max 1550 128GB can run Phi-4 14B with a A grade (Runs well). Expected decode speed: 196.0 tok/s.

How much VRAM does Phi-4 14B need?

Phi-4 14B (14B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.

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

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

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

On Intel Data Center GPU Max 1550 128GB, Phi-4 14B achieves approximately 196.0 tokens per second decode speed with a time-to-first-token of 988ms using Q4_K_M quantization.

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

For coding workloads, Phi-4 14B on Intel Data Center GPU Max 1550 128GB receives a A grade with 196.0 tok/s and 16K context.

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

On Intel Data Center GPU Max 1550 128GB, Phi-4 14B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for Phi-4 14B

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