Will It Run AI

Can Devstral 2 123B Instruct run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S96Excellent
Estimated from fit model

Devstral 2 123B Instruct needs ~94.1 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) — 94.1 GB, 29.2 tok/s, Runs well
94.1 GB required128.0 GB available
74% VRAM used

Fit status

Runs well

Decode

29.2 tok/s

TTFT

6626 ms

Safe context

117K

Memory

94.1 GB / 128.0 GB

Memory breakdown

Weights75.0 GB
KV Cache5.4 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct 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: 29.2 tok/s decode · 6.6s 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 well29.2 tok/s3614 ms117K
CodingSRuns well29.2 tok/s6626 ms117K
Agentic CodingSRuns well29.2 tok/s9637 ms117K
ReasoningSRuns well29.2 tok/s7830 ms117K
RAGSRuns well29.2 tok/s12046 ms117K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
48.0 GB
LowS88
Q3_K_S
3
60.3 GB
LowS90
NVFP4
4
68.9 GB
MediumS91
Q4_K_M
4
75.0 GB
MediumS91
Q5_K_M
5
88.6 GB
HighS91
Q6_KBest for your GPU
6
100.9 GB
HighS91
Q8_0
8
131.6 GB
Very HighF0
F16
16
252.2 GB
MaximumF0

Get started

Copy-paste commands to run Devstral 2 123B Instruct on your machine.

Run

lms load Devstral-2-123B-Instruct-2512 && lms server start

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Devstral 2 123B Instruct?

Yes, Intel Data Center GPU Max 1550 128GB can run Devstral 2 123B Instruct with a S grade (Runs well). Expected decode speed: 29.2 tok/s.

How much VRAM does Devstral 2 123B Instruct need?

Devstral 2 123B Instruct (123B parameters) requires approximately 94.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral 2 123B Instruct?

The recommended quantization for Devstral 2 123B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral 2 123B Instruct run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Devstral 2 123B Instruct achieves approximately 29.2 tokens per second decode speed with a time-to-first-token of 6626ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Devstral 2 123B Instruct for coding?

For coding workloads, Devstral 2 123B Instruct on Intel Data Center GPU Max 1550 128GB receives a S grade with 29.2 tok/s and 117K context.

What context window can Devstral 2 123B Instruct use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Devstral 2 123B Instruct can safely use up to 117K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral 2 123B Instruct 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 2 123B Instruct?

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 2 123B Instruct
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