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URL: https://willitrunai.com/can-run/granite-4.1-8b-on-arc-a770-16gb

⇱ Granite 4.1 8B on Intel Arc A770 16GB? YES


Can Granite 4.1 8B run on Intel Arc A770 16GB?

YES — Runs Great

A78Great
Estimated from fit model

Granite 4.1 8B needs ~9.8 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.8 GB, 55.5 tok/s, Runs well
9.8 GB required16.0 GB available
61% VRAM used

Fit status

Runs well

Decode

55.5 tok/s

TTFT

3488 ms

Safe context

56K

Memory

9.8 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on Intel Arc A770 16GB
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: 55.5 tok/s decode · 3.5s TTFT (warm) · 139 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 well55.5 tok/s1902 ms56K
CodingARuns well55.5 tok/s3488 ms56K
Agentic CodingARuns well55.5 tok/s5073 ms56K
ReasoningARuns well55.5 tok/s4122 ms56K
RAGARuns well55.5 tok/s6341 ms56K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowA71
Q3_K_S
3
3.9 GB
LowA72
NVFP4
4
4.5 GB
MediumA73
Q4_K_M
4
4.9 GB
MediumA73
Q5_K_M
5
5.8 GB
HighA74
Q6_K
6
6.6 GB
HighA75
Q8_0Best for your GPU
8
8.6 GB
Very HighA76
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Your hardware

More models your Intel Arc A770 16GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen 3.5 9B
9BS49.3 tok/s
👁 Alibaba
Qwen 3 14B
14BS31.9 tok/s
👁 Microsoft
Phi-4-reasoning-plus 14B
14.7BS30.2 tok/s
👁 OpenAI
GPT-OSS 20B
21BA29.2 tok/s
👁 Mistral
Ministral 3 14B
14BS31.7 tok/s

Frequently asked questions

See all results for Intel Arc A770 16GBSee all hardware for Granite 4.1 8B