Can Qwen 3.5 27B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

S95Excellent

Estimated from fit model

Qwen 3.5 27B needs ~23.7 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~25 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) — 23.7 GB, 24.8 tok/s, Runs well

23.7 GB required32.0 GB available

74% VRAM used

Fit status

Runs well

Decode

24.8 tok/s

TTFT

7819 ms

Safe context

58K

Memory

23.7 GB / 32.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 3.5 27B 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: 24.8 tok/s decode · 7.8s TTFT (warm) · 62 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	24.8 tok/s	4265 ms	58K
Coding	S	Runs well	24.8 tok/s	7819 ms	58K
Agentic Coding	S	Tight fit	24.8 tok/s	11373 ms	58K
Reasoning	S	Runs well	24.8 tok/s	9240 ms	58K
RAG	S	Tight fit	24.8 tok/s	14216 ms	58K

Quantization options

How Qwen 3.5 27B (27B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	S89
Q3_K_S	3	13.2 GB	Low	S90
NVFP4	4	15.1 GB	Medium	S91
Q4_K_M	4	16.5 GB	Medium	S92
Q5_K_M	5	19.4 GB	High	S92
Q6_KBest for your GPU	6	22.1 GB	High	S91
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 27B on your machine.

Run

ollama run qwen3.5:27b

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

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Qwen 3.5 27B?

Yes, Radeon AI PRO R9700 32GB can run Qwen 3.5 27B with a S grade (Runs well). Expected decode speed: 24.8 tok/s.

How much VRAM does Qwen 3.5 27B need?

Qwen 3.5 27B (27B parameters) requires approximately 23.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 27B?

The recommended quantization for Qwen 3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 27B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 3.5 27B achieves approximately 24.8 tokens per second decode speed with a time-to-first-token of 7819ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Qwen 3.5 27B for coding?

For coding workloads, Qwen 3.5 27B on Radeon AI PRO R9700 32GB receives a S grade with 24.8 tok/s and 58K context.

What context window can Qwen 3.5 27B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 3.5 27B can safely use up to 58K 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 27B

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