Will It Run AI

Can Command A 111B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S93Excellent
Estimated from fit model

Command A 111B needs ~85.3 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~30 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) — 85.3 GB, 32.5 tok/s, Runs well
85.3 GB required128.0 GB available
67% VRAM used

Fit status

Runs well

Decode

32.5 tok/s

TTFT

5956 ms

Safe context

191K

Memory

85.3 GB / 128.0 GB

Memory breakdown

Weights67.7 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsCommand A 111B 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: 32.5 tok/s decode · 6.0s TTFT (warm) · 81 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 well29.8 tok/s3547 ms191K
CodingSRuns well29.8 tok/s6502 ms191K
Agentic CodingSRuns well29.8 tok/s9458 ms191K
ReasoningSRuns well29.8 tok/s7685 ms191K
RAGSRuns well29.8 tok/s11822 ms191K

Quantization options

How Command A 111B (111B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
43.3 GB
LowA84
Q3_K_S
3
54.4 GB
LowS86
NVFP4
4
62.2 GB
MediumS87
Q4_K_M
4
67.7 GB
MediumS88
Q5_K_M
5
79.9 GB
HighS88
Q6_KBest for your GPU
6
91.0 GB
HighS88
Q8_0
8
118.8 GB
Very HighF0
F16
16
227.6 GB
MaximumF0

Get started

Copy-paste commands to run Command A 111B on your machine.

Run

ollama run command-a

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
MistralMistral Small 4 119B119BS87.9 tok/s
OpenAIGPT-OSS 120B117BS30.7 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Command A 111B?

Yes, Intel Data Center GPU Max 1550 128GB can run Command A 111B with a S grade (Runs well). Expected decode speed: 29.8 tok/s.

How much VRAM does Command A 111B need?

Command A 111B (111B parameters) requires approximately 85.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Command A 111B?

The recommended quantization for Command A 111B is Q4_K_M, which balances quality and memory efficiency.

What speed will Command A 111B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Command A 111B achieves approximately 29.8 tokens per second decode speed with a time-to-first-token of 6502ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Command A 111B for coding?

For coding workloads, Command A 111B on Intel Data Center GPU Max 1550 128GB receives a S grade with 29.8 tok/s and 191K context.

What context window can Command A 111B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Command A 111B can safely use up to 191K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Command A 111B 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 Command A 111B?

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 Command A 111B
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