Can Qwen 3.5 9B run on RTX 3080 10GB?

YES — With Offload

S95Excellent

Estimated from fit model

Qwen 3.5 9B needs ~9.9 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~113 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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) — 9.9 GB, 113.1 tok/s, Runs with offload

9.9 GB required10.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

113.1 tok/s

TTFT

1712 ms

Safe context

17K

Memory

9.9 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on RTX 3080 10GB
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: 113.1 tok/s decode · 1.7s TTFT (warm) · 283 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	113.1 tok/s	934 ms	17K
Coding	S	Runs with offload	113.1 tok/s	1712 ms	17K
Agentic Coding	F	Too heavy	56.9 tok/s	4945 ms	17K
Reasoning	S	Runs with offload	113.1 tok/s	2023 ms	17K
RAG	F	Too heavy	56.9 tok/s	6182 ms	17K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S94
Q3_K_S	3	4.4 GB	Low	S95
NVFP4	4

Get started

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

Run

ollama run qwen3.5:9b

Frequently asked questions

Can RTX 3080 10GB run Qwen 3.5 9B?

Yes, RTX 3080 10GB can run Qwen 3.5 9B with a S grade (Runs with offload). Expected decode speed: 113.1 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 9.9 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 RTX 3080 10GB?

On RTX 3080 10GB, Qwen 3.5 9B achieves approximately 113.1 tokens per second decode speed with a time-to-first-token of 1712ms using Q4_K_M quantization.

Can RTX 3080 10GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RTX 3080 10GB receives a S grade with 113.1 tok/s and 17K context.

What context window can Qwen 3.5 9B use on RTX 3080 10GB?

On RTX 3080 10GB, Qwen 3.5 9B can safely use up to 17K 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 RTX 3080 10GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3080 10GB See all hardware for Qwen 3.5 9B

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