Can Granite 4.1 8B run on RTX 5000 Ada Laptop 16GB?

YES — Runs Great

A80Great

○Estimated from fit model

Granite 4.1 8B needs ~10.1 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~93 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 10.1 GB, 92.6 tok/s, Runs well

10.1 GB required16.0 GB available

63% VRAM used

Fit status

Runs well

Decode

92.6 tok/s

TTFT

2090 ms

Safe context

55K

Memory

10.1 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on RTX 5000 Ada Laptop 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: 92.6 tok/s decode · 2.1s TTFT (warm) · 232 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	92.6 tok/s	1140 ms	55K
Coding	A	Runs well	92.6 tok/s	2090 ms	55K
Agentic Coding	A	Runs well	92.6 tok/s	3040 ms	55K
Reasoning	A	Runs well	92.6 tok/s	2470 ms	55K
RAG	A	Runs well	92.6 tok/s	3800 ms	55K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A71
Q3_K_S	3	3.9 GB	Low	A72
NVFP4	4	4.5 GB	Medium	A73
Q4_K_M	4	4.9 GB	Medium	A73
Q5_K_M	5	5.8 GB	High	A74
Q6_K	6	6.6 GB	High	A75
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 5000 Ada Laptop 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	82.3 tok/s
👁 Alibaba Qwen 3 14B	14B	S	53.2 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	50.4 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	47 tok/s
👁 Mistral Ministral 3 14B	14B	S	52.9 tok/s

Frequently asked questions

See all results for RTX 5000 Ada Laptop 16GB See all hardware for Granite 4.1 8B

URL: https://willitrunai.com/can-run/granite-4.1-8b-on-rtx-5000-ada-laptop-16gb

⇱ Granite 4.1 8B on RTX 5000 Ada Laptop 16GB? YES