Can Qwen 3.5 9B run on RTX 2000 Ada 16GB?

YES — Runs Great

S95Excellent

○Estimated from fit model

Qwen 3.5 9B needs ~10.5 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.5 GB, 42.9 tok/s, Runs well

10.5 GB required16.0 GB available

66% VRAM used

Fit status

Runs well

Decode

42.9 tok/s

TTFT

4517 ms

Safe context

56K

Memory

10.5 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on RTX 2000 Ada 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: 42.9 tok/s decode · 4.5s TTFT (warm) · 107 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	39.9 tok/s	2649 ms	56K
Coding	S	Runs well	39.9 tok/s	4856 ms	56K
Agentic Coding	S	Runs well	39.9 tok/s	7063 ms	56K
Reasoning	S	Runs well	42.9 tok/s	5338 ms	56K
RAG	S	Runs well	39.9 tok/s	8829 ms	56K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S89
Q3_K_S	3	4.4 GB	Low	S90
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.5 9B on your machine.

Run

ollama run qwen3.5:9b

Frequently asked questions

See all results for RTX 2000 Ada 16GB See all hardware for Qwen 3.5 9B

URL: https://willitrunai.com/can-run/qwen-3.5-9b-on-rtx-2000-ada-16gb

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