Will It Run AI

Can Mistral Small 4 119B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S97Excellent
Estimated from fit model

Mistral Small 4 119B needs ~91.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~88 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) — 91.7 GB, 87.9 tok/s, Runs well
91.7 GB required128.0 GB available
72% VRAM used

Fit status

Runs well

Decode

87.9 tok/s

TTFT

2203 ms

Safe context

124K

Memory

91.7 GB / 128.0 GB

Memory breakdown

Weights72.6 GB
KV Cache5.4 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsMistral Small 4 119B 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: 87.9 tok/s decode · 2.2s TTFT (warm) · 220 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 well87.9 tok/s1202 ms124K
CodingSRuns well87.9 tok/s2203 ms124K
Agentic CodingSRuns well87.9 tok/s3205 ms124K
ReasoningSRuns well87.9 tok/s2604 ms124K
RAGSRuns well87.9 tok/s4006 ms124K

Quantization options

How Mistral Small 4 119B (119B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
46.4 GB
LowA85
Q3_K_S
3
58.3 GB
LowS87
NVFP4
4
66.6 GB
MediumS88
Q4_K_M
4
72.6 GB
MediumS88
Q5_K_M
5
85.7 GB
HighS88
Q6_KBest for your GPU
6
97.6 GB
HighS88
Q8_0
8
127.3 GB
Very HighF0
F16
16
244.0 GB
MaximumF0

Get started

Copy-paste commands to run Mistral Small 4 119B on your machine.

Run

lms load Mistral-Small-4-119B-2603 && 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
AlibabaQwen 3.5 122B A10B122BS81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Mistral Small 4 119B?

Yes, Intel Data Center GPU Max 1550 128GB can run Mistral Small 4 119B with a S grade (Runs well). Expected decode speed: 87.9 tok/s.

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B (119B parameters) requires approximately 91.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 4 119B?

The recommended quantization for Mistral Small 4 119B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 4 119B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Mistral Small 4 119B achieves approximately 87.9 tokens per second decode speed with a time-to-first-token of 2203ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Mistral Small 4 119B for coding?

For coding workloads, Mistral Small 4 119B on Intel Data Center GPU Max 1550 128GB receives a S grade with 87.9 tok/s and 124K context.

What context window can Mistral Small 4 119B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Mistral Small 4 119B can safely use up to 124K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 4 119B 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 Mistral Small 4 119B?

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 Mistral Small 4 119B
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