Can Granite 4.1 3B run on RTX 4050 Laptop 6GB?

YES — Runs Great

A72Great

Estimated from fit model

Granite 4.1 3B needs ~4.6 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 4.6 GB, 48.0 tok/s, Runs well

4.6 GB required6.0 GB available

77% VRAM used

Fit status

Runs well

Decode

48.0 tok/s

TTFT

4033 ms

Safe context

35K

Memory

4.6 GB / 6.0 GB

Memory breakdown

Weights1.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsGranite 4.1 3B on RTX 4050 Laptop 6GB
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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 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	42.0 tok/s	2514 ms	35K
Coding	A	Runs well	48.0 tok/s	4033 ms	35K
Agentic Coding	B	Runs with offload	48.0 tok/s	5867 ms	35K
Reasoning	A	Runs well	48.0 tok/s	4767 ms	35K
RAG	B	Runs with offload	48.0 tok/s	7333 ms	35K

Quantization options

How Granite 4.1 3B (3B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A70
Q3_K_S	3	1.5 GB	Low	A71
NVFP4	4	1.7 GB	Medium	A72
Q4_K_M	4	1.8 GB	Medium	A72
Q5_K_M	5	2.2 GB	High	A71
Q6_K	6	2.5 GB	High	A71
Q8_0Best for your GPU	8	3.2 GB	Very High	A71
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 4.1 3B on your machine.

Run

ollama run granite4.1:3b

Your hardware

More models your RTX 4050 Laptop 6GB can run

Model	Params	Grade	Decode
Qwen 3.5 4B	4B	S	40.6 tok/s
Phi-4 Mini Reasoning 4B	3.8B	S	59.8 tok/s
Qwen 3 4B	4B	A	40.6 tok/s
Qwen 2.5 VL 7B	7B	B	20 tok/s
Qwen 2.5 7B	7B	B	20 tok/s

Frequently asked questions

Can RTX 4050 Laptop 6GB run Granite 4.1 3B?

Yes, RTX 4050 Laptop 6GB can run Granite 4.1 3B with a A grade (Runs well). Expected decode speed: 48.0 tok/s.

How much VRAM does Granite 4.1 3B need?

Granite 4.1 3B (3B parameters) requires approximately 4.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 3B?

The recommended quantization for Granite 4.1 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 3B run at on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Granite 4.1 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can RTX 4050 Laptop 6GB run Granite 4.1 3B for coding?

For coding workloads, Granite 4.1 3B on RTX 4050 Laptop 6GB receives a A grade with 48.0 tok/s and 35K context.

What context window can Granite 4.1 3B use on RTX 4050 Laptop 6GB?

On RTX 4050 Laptop 6GB, Granite 4.1 3B can safely use up to 35K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4050 Laptop 6GB See all hardware for Granite 4.1 3B

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