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⇱ Granite Code 8B on Intel Arc Pro A60 12GB? YES


Can Granite Code 8B run on Intel Arc Pro A60 12GB?

YES — Runs Great

A79Great
Estimated from fit model

Granite Code 8B needs ~8.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 8.9 GB, 41.4 tok/s, Runs well
8.9 GB required12.0 GB available
74% VRAM used

Fit status

Runs well

Decode

41.4 tok/s

TTFT

4671 ms

Safe context

8K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsGranite Code 8B on Intel Arc Pro A60 12GB
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: 41.4 tok/s decode · 4.7s TTFT (warm) · 104 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 well41.4 tok/s2548 ms8K
CodingARuns well41.4 tok/s4671 ms8K
Agentic CodingATight fit41.4 tok/s6794 ms8K
ReasoningARuns well41.4 tok/s5520 ms8K
RAGATight fit41.4 tok/s8492 ms8K

Quantization options

How Granite Code 8B (8B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowA75
Q3_K_S
3
3.9 GB
LowA76
NVFP4
4
4.5 GB
MediumA76
Q4_K_M
4
4.9 GB
MediumA77
Q5_K_M
5
5.8 GB
HighA78
Q6_K
6
6.6 GB
HighA77
Q8_0Best for your GPU
8
8.6 GB
Very HighA77
F16
16
16.4 GB
MaximumF0

Get started

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

Run

ollama run granite-code:8b

Your hardware

More models your Intel Arc Pro A60 12GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen 3.5 9B
9BS36.8 tok/s
👁 Alibaba
Qwen 3 14B
14BA14.9 tok/s
👁 Microsoft
Phi-4-reasoning-plus 14B
14.7BA12 tok/s
👁 Mistral
Ministral 3 14B
14BA14.8 tok/s
👁 Microsoft
Phi-4 14B
14BB13.5 tok/s

Frequently asked questions

See all results for Intel Arc Pro A60 12GBSee all hardware for Granite Code 8B