Will It Run AI

Can Granite 3.1 8B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C50Usable
Estimated from fit model

Granite 3.1 8B needs ~20.5 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~112 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) — 20.5 GB, 112.0 tok/s, Runs well
20.5 GB required128.0 GB available
16% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

128K

Memory

20.5 GB / 128.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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
ChatCRuns well112.0 tok/s943 ms128K
CodingCRuns well112.0 tok/s1729 ms128K
Agentic CodingCRuns well112.0 tok/s2514 ms128K
ReasoningCRuns well112.0 tok/s2043 ms128K
RAGCRuns well112.0 tok/s3143 ms128K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC43
Q3_K_S
3
3.9 GB
LowC43
NVFP4
4
4.5 GB
MediumC43
Q4_K_M
4
4.9 GB
MediumC43
Q5_K_M
5
5.8 GB
HighC43
Q6_K
6
6.6 GB
HighC43
Q8_0
8
8.6 GB
Very HighC43
F16Best for your GPU
16
16.4 GB
MaximumC43

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Opciones de mejora

Hardware que ejecuta bien Granite 3.1 8B

Mac Studio M3 Ultra 256GBOpción económica
256 GB Unified (+128)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.112 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Granite 3.1 8B?

Yes, Intel Data Center GPU Max 1550 128GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 20.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Granite 3.1 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Intel Data Center GPU Max 1550 128GB receives a C grade with 112.0 tok/s and 128K context.

What context window can Granite 3.1 8B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Granite 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 3.1 8B 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 Granite 3.1 8B?

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 Granite 3.1 8B
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