Can Granite 4.1 3B run on Intel Arc B580 12GB?

YES — Runs Great

B66Good

○Estimated from fit model

Granite 4.1 3B needs ~5.2 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 5.2 GB, 42.0 tok/s, Runs well

5.2 GB required12.0 GB available

43% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

106K

Memory

5.2 GB / 12.0 GB

Memory breakdown

Weights1.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGranite 4.1 3B on Intel Arc B580 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	42.0 tok/s	2514 ms	106K
Coding	B	Runs well	42.0 tok/s	4610 ms	106K
Agentic Coding	B	Runs well	42.0 tok/s	6705 ms	106K
Reasoning	B	Runs well	42.0 tok/s	5448 ms	106K
RAG	B	Runs well	42.0 tok/s	8381 ms	106K

Quantization options

How Granite 4.1 3B (3B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B64
Q3_K_S	3	1.5 GB	Low	B65
NVFP4	4	1.7 GB	Medium	B65
Q4_K_M	4	1.8 GB	Medium	B65
Q5_K_M	5	2.2 GB	High	B65
Q6_K	6	2.5 GB	High	B66
Q8_0	8	3.2 GB	Very High	B67
F16Best for your GPU	16	6.1 GB	Maximum	B69