Can Qwen 3.5 9B run on RX 9060 8GB?

BARELY — Tight on Memory

A80Great

Estimated from fit model

Qwen 3.5 9B needs ~9.4 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 9.4 GB, 19.0 tok/s, Very compromised (needs ~0.8 GB host RAM)

9.4 GB required8.0 GB available

118% VRAM needed

1.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.8 GB host RAM)

Decode

19.0 tok/s

TTFT

10173 ms

Safe context

Memory

9.4 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on RX 9060 8GB
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: 19.0 tok/s decode · 10.2s TTFT (warm) · 48 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload (needs ~0.2 GB host RAM)	24.7 tok/s	4270 ms	6K
Coding	A	Very compromised	17.7 tok/s	10936 ms	6K
Agentic Coding	F	Too heavy	12.2 tok/s	23039 ms	6K
Reasoning	A	Very compromised (needs ~0.8 GB host RAM)	19.0 tok/s	12023 ms	6K
RAG	F	Too heavy	12.2 tok/s	28799 ms

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S95
Q3_K_S	3	4.4 GB	Low	S95
NVFP4Best for your GPU

Get started

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

Run

ollama run qwen3.5:9b

Frequently asked questions

Can RX 9060 8GB run Qwen 3.5 9B?

Yes, RX 9060 8GB can run Qwen 3.5 9B with a A grade (Very compromised). Expected decode speed: 17.7 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 9.4 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 RX 9060 8GB?

On RX 9060 8GB, Qwen 3.5 9B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10936ms using Q4_K_M quantization.

Can RX 9060 8GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RX 9060 8GB receives a A grade with 17.7 tok/s and 6K context.

What context window can Qwen 3.5 9B use on RX 9060 8GB?

On RX 9060 8GB, Qwen 3.5 9B can safely use up to 6K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 9B feels slow on RX 9060 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 9060 8GB See all hardware for Qwen 3.5 9B

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