Can Pixtral Large 124B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S93Excellent

○Estimated from fit model

Pixtral Large 124B needs ~94.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~29 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) — 94.7 GB, 29.0 tok/s, Runs well

94.7 GB required128.0 GB available

74% VRAM used

Fit status

Runs well

Decode

29.0 tok/s

TTFT

6679 ms

Safe context

115K

Memory

94.7 GB / 128.0 GB

Memory breakdown

Weights75.6 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsPixtral Large 124B 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: 29.0 tok/s decode · 6.7s TTFT (warm) · 73 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	S	Runs well	29.0 tok/s	3643 ms	115K
Coding	S	Runs well	29.0 tok/s	6679 ms	115K
Agentic Coding	S	Runs well	29.0 tok/s	9715 ms	115K
Reasoning	S	Runs well	29.0 tok/s	7894 ms	115K
RAG	S	Runs well	29.0 tok/s	12144 ms	115K

Quantization options

How Pixtral Large 124B (124B 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	48.4 GB	Low	A84
Q3_K_S	3	60.8 GB	Low	S86
NVFP4	4

Get started

Copy-paste commands to run Pixtral Large 124B on your machine.

Run

lms load Pixtral-Large-Instruct-2411 && lms server start

Frequently asked questions

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for Pixtral Large 124B

URL: https://willitrunai.com/can-run/pixtral-large-124b-on-max-1550-128gb

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