Will It Run AI

Can Gemma 4 26B A4B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A82Great
Estimated from fit model

Gemma 4 26B A4B needs ~32.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~327 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) — 32.7 GB, 327.4 tok/s, Runs well
32.7 GB required128.0 GB available
26% VRAM used

Fit status

Runs well

Decode

327.4 tok/s

TTFT

591 ms

Safe context

256K

Memory

32.7 GB / 128.0 GB

Memory breakdown

Weights15.4 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B 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: 327.4 tok/s decode · 591ms TTFT (warm) · 818 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
ChatARuns well327.4 tok/s350 ms256K
CodingARuns well327.4 tok/s591 ms256K
Agentic CodingARuns well327.4 tok/s860 ms256K
ReasoningARuns well327.4 tok/s699 ms256K
RAGARuns well327.4 tok/s1075 ms256K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.8 GB
LowA73
Q3_K_S
3
12.3 GB
LowA74
NVFP4
4
14.1 GB
MediumA74
Q4_K_M
4
15.4 GB
MediumA74
Q5_K_M
5
18.1 GB
HighA74
Q6_K
6
20.7 GB
HighA74
Q8_0
8
27.0 GB
Very HighA75
F16Best for your GPU
16
51.7 GB
MaximumA79

Get started

Copy-paste commands to run Gemma 4 26B A4B on your machine.

Run

ollama run gemma4:26b

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 Gemma 4 26B A4B?

Yes, Intel Data Center GPU Max 1550 128GB can run Gemma 4 26B A4B with a A grade (Runs well). Expected decode speed: 327.4 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 32.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Gemma 4 26B A4B achieves approximately 327.4 tokens per second decode speed with a time-to-first-token of 591ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on Intel Data Center GPU Max 1550 128GB receives a A grade with 327.4 tok/s and 256K context.

What context window can Gemma 4 26B A4B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Gemma 4 26B A4B can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 26B A4B 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 Gemma 4 26B A4B?

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 Gemma 4 26B A4B
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