Can Qwen 3.5 35B A3B run on RX 9060 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3.5 35B A3B needs ~24.5 GB but RX 9060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 24.5 GB, exceeds 8.0 GB available

24.5 GB required8.0 GB available

306% VRAM needed

16.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.8 tok/s

TTFT

51472 ms

Safe context

Memory

24.5 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 35B A3B 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: 3.8 tok/s decode · 51.5s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 24.5 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.8 tok/s	28076 ms	4K
Coding	F	Too heavy	3.5 tok/s	55976 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	74868 ms	4K
Reasoning	F	Too heavy	3.8 tok/s	60831 ms	4K
RAG	F	Too heavy	3.8 tok/s	93586 ms	4K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	F0
Q3_K_S	3	17.2 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen 3.5 35B A3B well

RX 7900 XTX 24GBBest value

24 GB VRAM (+16)960 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.60 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 1479%.

~$999 MSRP

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+24)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.52.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade

32 GB VRAM (+24)512 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.39.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,249 MSRP

Frequently asked questions

Can RX 9060 8GB run Qwen 3.5 35B A3B?

No, Qwen 3.5 35B A3B requires more memory than RX 9060 8GB provides.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on RX 9060 8GB?

On RX 9060 8GB, Qwen 3.5 35B A3B achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 55976ms using Q4_K_M quantization.

Can RX 9060 8GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on RX 9060 8GB receives a F grade with 3.5 tok/s and 4K context.

What context window can Qwen 3.5 35B A3B use on RX 9060 8GB?

On RX 9060 8GB, Qwen 3.5 35B A3B can safely use up to 4K 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 35B A3B feels slow on RX 9060 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 9060 8GB See all hardware for Qwen 3.5 35B A3B

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