Will It Run AI

Can Devstral Small 1.1 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S87Excellent
Estimated from fit model

Devstral Small 1.1 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
Share:

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 feelsDevstral Small 1.1 on Intel Data Center GPU Max 1550 128GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
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 well137.7 tok/s2045 ms131K
ReasoningSRuns well148.0 tok/s1546 ms131K
RAGSRuns well148.0 tok/s2378 ms131K

Quantization options

How Devstral Small 1.1 (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
LowA78
Q3_K_S
3
11.8 GB
LowA78
NVFP4
4
13.4 GB
MediumA78
Q4_K_M
4
14.6 GB
MediumA78
Q5_K_M
5
17.3 GB
HighA78
Q6_K
6
19.7 GB
HighA79
Q8_0
8
25.7 GB
Very HighA79
F16Best for your GPU
16
49.2 GB
MaximumA83

Get started

Copy-paste commands to run Devstral Small 1.1 on your machine.

Run

lms load Devstral-Small-2507 && lms server start

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 Devstral Small 1.1?

Yes, Intel Data Center GPU Max 1550 128GB can run Devstral Small 1.1 with a S grade (Runs well). Expected decode speed: 148.0 tok/s.

How much VRAM does Devstral Small 1.1 need?

Devstral Small 1.1 (24B parameters) requires approximately 30.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral Small 1.1?

The recommended quantization for Devstral Small 1.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral Small 1.1 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Devstral Small 1.1 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 Devstral Small 1.1 for coding?

For coding workloads, Devstral Small 1.1 on Intel Data Center GPU Max 1550 128GB receives a S grade with 148.0 tok/s and 131K context.

What context window can Devstral Small 1.1 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Devstral Small 1.1 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 Devstral Small 1.1 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 Devstral Small 1.1?

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 Devstral Small 1.1
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/devstral-small-2507-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: