Can Qwen 3.5 4B run on RTX 2080 Ti 11GB?

YES — Runs Great

S94Excellent

○Estimated from fit model

Qwen 3.5 4B needs ~6.9 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~56 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) — 6.9 GB, 56.0 tok/s, Runs well

6.9 GB required11.0 GB available

63% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

46K

Memory

6.9 GB / 11.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsQwen 3.5 4B on RTX 2080 Ti 11GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	56.0 tok/s	1886 ms	46K
Coding	S	Runs well	56.0 tok/s	3457 ms	46K
Agentic Coding	S	Tight fit	56.0 tok/s	5029 ms	46K
Reasoning	S	Runs well	56.0 tok/s	4086 ms	46K
RAG	S	Tight fit	56.0 tok/s	6286 ms	46K

Quantization options

How Qwen 3.5 4B (4B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	S88
Q3_K_S	3	2.0 GB	Low	S89
NVFP4	4	2.2 GB	Medium	S89
Q4_K_M	4	2.4 GB	Medium	S89
Q5_K_M	5	2.9 GB	High	S90
Q6_K	6	3.3 GB	High	S91
Q8_0Best for your GPU	8	4.3 GB	Very High	S92
F16	16	8.2 GB	Maximum	F0

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 2080 Ti 11GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	78.4 tok/s

Frequently asked questions

See all results for RTX 2080 Ti 11GB See all hardware for Qwen 3.5 4B

URL: https://willitrunai.com/can-run/qwen-3.5-4b-on-rtx-2080-ti-11gb