Can Granite 4.1 8B run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

A80Great

○Estimated from fit model

Granite 4.1 8B needs ~9.7 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.7 GB, 69.5 tok/s, Runs well

9.7 GB required12.0 GB available

81% VRAM used

Fit status

Runs well

Decode

69.5 tok/s

TTFT

2787 ms

Safe context

31K

Memory

9.7 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on RTX 4000 Ada Laptop 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: 69.5 tok/s decode · 2.8s TTFT (warm) · 174 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	69.5 tok/s	1520 ms	31K
Coding	A	Runs well	69.5 tok/s	2787 ms	31K
Agentic Coding	A	Runs with offload (needs ~0.1 GB host RAM)	50.6 tok/s	5560 ms	31K
Reasoning	A	Runs well	69.5 tok/s	3294 ms	31K
RAG	A	Runs with offload (needs ~0.1 GB host RAM)	50.6 tok/s	6950 ms	31K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A74
Q3_K_S	3	3.9 GB	Low	A75
NVFP4	4	4.5 GB	Medium	A76
Q4_K_M	4	4.9 GB	Medium	A76
Q5_K_M	5	5.8 GB	High	A77
Q6_K	6	6.6 GB	High	A76
Q8_0Best for your GPU	8	8.6 GB	Very High	A76
F16	16	16.4 GB	Maximum	F0

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 RTX 4000 Ada Laptop 12GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	61.8 tok/s
👁 Alibaba Qwen 3 14B	14B	A	23.8 tok/s
👁 Mistral Ministral 3 14B	14B	A	23.7 tok/s
👁 Microsoft Phi-4 14B	14B	A	21.5 tok/s
👁 Alibaba Qwen 2.5 14B	14B	A	22.1 tok/s

Frequently asked questions

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Granite 4.1 8B

URL: https://willitrunai.com/can-run/granite-4.1-8b-on-rtx-4000-ada-laptop-12gb

⇱ Granite 4.1 8B on RTX 4000 Ada Laptop 12GB? YES