Can Qwen 3.5 4B run on RTX 4060 Laptop 8GB?

YES — Runs Great

S95Excellent

○Estimated from fit model

Qwen 3.5 4B needs ~6.3 GB VRAM. RTX 4060 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 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) — 6.3 GB, 64.0 tok/s, Runs well

6.3 GB required8.0 GB available

79% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

28K

Memory

6.3 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 4B on RTX 4060 Laptop 8GB
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: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 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	S	Runs well	56.0 tok/s	1886 ms	28K
Coding	S	Runs well	56.0 tok/s	3457 ms	28K
Agentic Coding	A	Runs with offload	51.5 tok/s	5468 ms	28K
Reasoning	S	Runs well	56.0 tok/s	4086 ms	28K
RAG	A	Runs with offload	51.5 tok/s	6834 ms	28K

Quantization options

How Qwen 3.5 4B (4B params) fits at each quantization level on RTX 4060 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	S91
Q3_K_S	3	2.0 GB	Low	S92
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.5 4B on your machine.

Run

ollama run qwen3.5:4b

Your hardware

More models your RTX 4060 Laptop 8GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	A	18.5 tok/s

Frequently asked questions

See all results for RTX 4060 Laptop 8GB See all hardware for Qwen 3.5 4B

URL: https://willitrunai.com/can-run/qwen-3.5-4b-on-rtx-4060-laptop-8gb

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