Can Qwen 3.5 9B run on Radeon PRO W7700 16GB?

YES — Runs Great

S96Excellent

○Estimated from fit model

Qwen 3.5 9B needs ~10.2 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~67 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) — 10.2 GB, 66.5 tok/s, Runs well

10.2 GB required16.0 GB available

64% VRAM used

Fit status

Runs well

Decode

66.5 tok/s

TTFT

2909 ms

Safe context

58K

Memory

10.2 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on Radeon PRO W7700 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: 66.5 tok/s decode · 2.9s TTFT (warm) · 166 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	66.5 tok/s	1587 ms	58K
Coding	S	Runs well	66.5 tok/s	2909 ms	58K
Agentic Coding	S	Runs well	66.5 tok/s	4232 ms	58K
Reasoning	S	Runs well	66.5 tok/s	3438 ms	58K
RAG	S	Runs well	66.5 tok/s	5290 ms	58K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on Radeon PRO W7700 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	5.0 GB	Medium	S90
Q4_K_M	4	5.5 GB	Medium	S91
Q5_K_M	5	6.5 GB	High	S92
Q6_K	6	7.4 GB	High	S93
Q8_0Best for your GPU	8	9.6 GB	Very High	S93
F16	16	18.5 GB	Maximum	F0

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 Radeon PRO W7700 16GB See all hardware for Qwen 3.5 9B

URL: https://willitrunai.com/can-run/qwen-3.5-9b-on-radeon-pro-w7700-16gb

⇱ Qwen 3.5 9B on Radeon PRO W7700 16GB? YES