Will It Run AI

Can Magistral Small 2507 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S88Excellent
Estimated from fit model

Magistral Small 2507 needs ~30.8 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~148 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) — 30.8 GB, 148.0 tok/s, Runs well
30.8 GB required128.0 GB available
24% VRAM used

Fit status

Runs well

Decode

148.0 tok/s

TTFT

1308 ms

Safe context

131K

Memory

30.8 GB / 128.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 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: 148.0 tok/s decode · 1.3s TTFT (warm) · 370 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 well148.0 tok/s713 ms131K
CodingSRuns well148.0 tok/s1308 ms131K
Agentic CodingSRuns well148.0 tok/s1902 ms131K
ReasoningSRuns well148.0 tok/s1546 ms131K
RAGSRuns well148.0 tok/s2378 ms131K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA80
Q3_K_S
3
11.8 GB
LowA80
NVFP4
4
13.4 GB
MediumA80
Q4_K_M
4
14.6 GB
MediumA80
Q5_K_M
5
17.3 GB
HighA80
Q6_K
6
19.7 GB
HighA81
Q8_0
8
25.7 GB
Very HighA81
F16Best for your GPU
16
49.2 GB
MaximumS85

Get started

Copy-paste commands to run Magistral Small 2507 on your machine.

Run

ollama run magistral

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 Magistral Small 2507?

Yes, Intel Data Center GPU Max 1550 128GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 148.0 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 30.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Magistral Small 2507?

The recommended quantization for Magistral Small 2507 is Q4_K_M, which balances quality and memory efficiency.

What speed will Magistral Small 2507 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Magistral Small 2507 achieves approximately 148.0 tokens per second decode speed with a time-to-first-token of 1308ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on Intel Data Center GPU Max 1550 128GB receives a S grade with 148.0 tok/s and 131K context.

What context window can Magistral Small 2507 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Magistral Small 2507 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Magistral Small 2507 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 Magistral Small 2507?

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 Magistral Small 2507
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