Can Qwen 3.5 9B run on GTX 1660 Super 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3.5 9B needs ~9.5 GB but GTX 1660 Super 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: 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) — 9.5 GB, exceeds 6.0 GB available

9.5 GB required6.0 GB available

158% VRAM needed

3.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.7 tok/s

TTFT

20062 ms

Safe context

Memory

9.5 GB / 6.0 GB

Offload

40%

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 9B on GTX 1660 Super 6GB
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: 9.7 tok/s decode · 20.1s TTFT (warm) · 24 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 9.5 GB, but this setup only exposes 6.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	12.6 tok/s	8351 ms	4K
Coding	F	Too heavy	9.0 tok/s	21567 ms	4K
Agentic Coding	F	Too heavy	6.1 tok/s	46108 ms	4K
Reasoning	F	Too heavy	9.7 tok/s	23710 ms	4K
RAG	F	Too heavy	6.1 tok/s	57635 ms	4K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	S95
Q3_K_S	3	4.4 GB	Low	F0

Upgrade options

Hardware that runs Qwen 3.5 9B well

RTX 3060 12GBBudget pick

12 GB VRAM (+6)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.46.5 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.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+10)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.54.4 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.41.2 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.

~$499 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run Qwen 3.5 9B?

No, Qwen 3.5 9B requires more memory than GTX 1660 Super 6GB provides.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 9.5 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 GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Qwen 3.5 9B achieves approximately 9.0 tokens per second decode speed with a time-to-first-token of 21567ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on GTX 1660 Super 6GB receives a F grade with 9.0 tok/s and 4K context.

What context window can Qwen 3.5 9B use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Qwen 3.5 9B 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 9B feels slow on GTX 1660 Super 6GB?

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 GTX 1660 Super 6GB See all hardware for Qwen 3.5 9B

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