Can Granite 3.1 8B run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

B60Good

Estimated from fit model

Granite 3.1 8B needs ~8.9 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 8.9 GB, 62.1 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

62.1 tok/s

TTFT

3116 ms

Safe context

41K

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 3.1 8B on RTX 3500 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: 62.1 tok/s decode · 3.1s TTFT (warm) · 155 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	B	Runs well	62.1 tok/s	1699 ms	41K
Coding	B	Runs well	62.1 tok/s	3116 ms	41K
Agentic Coding	B	Tight fit	62.1 tok/s	4532 ms	41K
Reasoning	B	Runs well	62.1 tok/s	3682 ms	41K
RAG	B	Tight fit	62.1 tok/s	5665 ms	41K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	B55
NVFP4	4	4.5 GB	Medium	B56
Q4_K_M	4	4.9 GB	Medium	B56
Q5_K_M	5	5.8 GB	High	B57
Q6_K	6	6.6 GB	High	B57
Q8_0Best for your GPU	8	8.6 GB	Very High	B56
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Granite 3.1 8B?

Yes, RTX 3500 Ada Laptop 12GB can run Granite 3.1 8B with a B grade (Runs well). Expected decode speed: 62.1 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Granite 3.1 8B achieves approximately 62.1 tokens per second decode speed with a time-to-first-token of 3116ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on RTX 3500 Ada Laptop 12GB receives a B grade with 62.1 tok/s and 41K context.

What context window can Granite 3.1 8B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Granite 3.1 8B can safely use up to 41K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Granite 3.1 8B

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