Can Qwen 3.5 4B run on RTX 4080 Super 16GB?

YES — Runs Great

S90Excellent

○Estimated from fit model

Qwen 3.5 4B needs ~7.1 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 7.1 GB, 64.0 tok/s, Runs well

7.1 GB required16.0 GB available

44% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

81K

Memory

7.1 GB / 16.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 4B on RTX 4080 Super 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: 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	64.0 tok/s	1650 ms	81K
Coding	S	Runs well	64.0 tok/s	3025 ms	81K
Agentic Coding	S	Runs well	64.0 tok/s	4400 ms	81K
Reasoning	S	Runs well	64.0 tok/s	3575 ms	81K
RAG	S	Runs well	64.0 tok/s	5500 ms	81K

Quantization options

How Qwen 3.5 4B (4B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	S86
Q3_K_S	3	2.0 GB	Low	S86
NVFP4	4	2.2 GB	Medium	S86
Q4_K_M	4	2.4 GB	Medium	S87
Q5_K_M	5	2.9 GB	High	S87
Q6_K	6	3.3 GB	High	S87
Q8_0	8	4.3 GB	Very High	S88
F16Best for your GPU	16	8.2 GB	Maximum	S92

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 4080 Super 16GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	115.5 tok/s
👁 Alibaba Qwen 3 14B	14B	S	88.4 tok/s

Frequently asked questions

See all results for RTX 4080 Super 16GB See all hardware for Qwen 3.5 4B

URL: https://willitrunai.com/can-run/qwen-3.5-4b-on-rtx-4080-super-16gb