Will It Run AI

Can Granite Code 20B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A76Great
Estimated from fit model

Granite Code 20B needs ~29.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~179 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) — 29.1 GB, 178.5 tok/s, Runs well
29.1 GB required128.0 GB available
23% VRAM used

Fit status

Runs well

Decode

178.5 tok/s

TTFT

1085 ms

Safe context

8K

Memory

29.1 GB / 128.0 GB

Memory breakdown

Weights12.2 GB
KV Cache3.2 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsGranite Code 20B 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: 178.5 tok/s decode · 1.1s TTFT (warm) · 446 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 well178.5 tok/s592 ms8K
CodingARuns well178.5 tok/s1085 ms8K
Agentic CodingARuns well178.5 tok/s1578 ms8K
ReasoningARuns well178.5 tok/s1282 ms8K
RAGARuns well178.5 tok/s1972 ms8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowB68
Q3_K_S
3
9.8 GB
LowB68
NVFP4
4
11.2 GB
MediumB68
Q4_K_M
4
12.2 GB
MediumB68
Q5_K_M
5
14.4 GB
HighB68
Q6_K
6
16.4 GB
HighB68
Q8_0
8
21.4 GB
Very HighB69
F16Best for your GPU
16
41.0 GB
MaximumA72

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

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 Granite Code 20B?

Yes, Intel Data Center GPU Max 1550 128GB can run Granite Code 20B with a A grade (Runs well). Expected decode speed: 178.5 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 29.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Granite Code 20B achieves approximately 178.5 tokens per second decode speed with a time-to-first-token of 1085ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on Intel Data Center GPU Max 1550 128GB receives a A grade with 178.5 tok/s and 8K context.

What context window can Granite Code 20B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Granite Code 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B 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 Code 20B?

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 Code 20B
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