Will It Run AI

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
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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) — 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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well29.0 tok/s3643 ms115K
CodingSRuns well29.0 tok/s6679 ms115K
Agentic CodingSRuns well29.0 tok/s9715 ms115K
ReasoningSRuns well29.0 tok/s7894 ms115K
RAGSRuns well29.0 tok/s12144 ms115K

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).

QuantBitsVRAMQualityFit
Q2_K
2
48.4 GB
LowA84
Q3_K_S
3
60.8 GB
LowS86
NVFP4
4
69.4 GB
MediumS87
Q4_K_M
4
75.6 GB
MediumS87
Q5_K_M
5
89.3 GB
HighS87
Q6_KBest for your GPU
6
101.7 GB
HighS87
Q8_0
8
132.7 GB
Very HighF0
F16
16
254.2 GB
MaximumF0

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

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

Yes, Intel Data Center GPU Max 1550 128GB can run Pixtral Large 124B with a S grade (Runs well). Expected decode speed: 29.0 tok/s.

How much VRAM does Pixtral Large 124B need?

Pixtral Large 124B (124B parameters) requires approximately 94.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Pixtral Large 124B?

The recommended quantization for Pixtral Large 124B is Q4_K_M, which balances quality and memory efficiency.

What speed will Pixtral Large 124B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Pixtral Large 124B achieves approximately 29.0 tokens per second decode speed with a time-to-first-token of 6679ms using Q4_K_M quantization.

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

For coding workloads, Pixtral Large 124B on Intel Data Center GPU Max 1550 128GB receives a S grade with 29.0 tok/s and 115K context.

What context window can Pixtral Large 124B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Pixtral Large 124B can safely use up to 115K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Pixtral Large 124B 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 Pixtral Large 124B?

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 Data Center GPU Max 1550 128GBSee all hardware for Pixtral Large 124B
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