Can Qwen 3.5 9B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Qwen 3.5 9B needs ~11.8 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~74 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) — 11.8 GB, 73.9 tok/s, Runs well

11.8 GB required32.0 GB available

37% VRAM used

Fit status

Runs well

Decode

73.9 tok/s

TTFT

2618 ms

Safe context

131K

Memory

11.8 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B 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: 73.9 tok/s decode · 2.6s TTFT (warm) · 185 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	73.9 tok/s	1428 ms	131K
Coding	S	Runs well	73.9 tok/s	2618 ms	131K
Agentic Coding	S	Runs well	73.9 tok/s	3809 ms	131K
Reasoning	S	Runs well	73.9 tok/s	3094 ms	131K
RAG	S	Runs well	73.9 tok/s	4761 ms	131K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A85
Q3_K_S	3	4.4 GB	Low	S85
NVFP4	4

Get started

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

Run

ollama run qwen3.5:9b

Your hardware

More models your Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.5 27B	27B	S	24.8 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Qwen 3.5 9B?

Yes, Radeon AI PRO R9700 32GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 73.9 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 3.5 9B achieves approximately 73.9 tokens per second decode speed with a time-to-first-token of 2618ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on Radeon AI PRO R9700 32GB receives a S grade with 73.9 tok/s and 131K context.

What context window can Qwen 3.5 9B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 3.5 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Qwen 3.5 9B

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