Can Qwen 3.5 35B A3B run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

S94Excellent

○Estimated from fit model

Qwen 3.5 35B A3B needs ~26.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 26.9 GB, 52.2 tok/s, Tight fit

26.9 GB required32.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

52.2 tok/s

TTFT

3710 ms

Safe context

72K

Memory

26.9 GB / 32.0 GB

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B on Radeon AI PRO R9700 32GB
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: 52.2 tok/s decode · 3.7s TTFT (warm) · 131 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	52.2 tok/s	2024 ms	72K
Coding	S	Tight fit	52.2 tok/s	3710 ms	72K
Agentic Coding	S	Tight fit	52.2 tok/s	5396 ms	72K
Reasoning	S	Tight fit	52.2 tok/s	4384 ms	72K
RAG	S	Tight fit	52.2 tok/s	6745 ms	72K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S90
Q3_K_S	3	17.2 GB	Low	S91
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Frequently asked questions

See all results for Radeon AI PRO R9700 32GB See all hardware for Qwen 3.5 35B A3B

URL: https://willitrunai.com/can-run/qwen-3.5-35b-a3b-on-radeon-ai-pro-r9700-32gb

⇱