Can Qwen 3.5 9B run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

S97Excellent

Estimated from fit model

Qwen 3.5 9B needs ~9.8 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~60 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 9.8 GB, 59.7 tok/s, Runs well

9.8 GB required12.0 GB available

82% VRAM used

Fit status

Runs well

Decode

59.7 tok/s

TTFT

3246 ms

Safe context

32K

Memory

9.8 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on RTX 4000 Ada Laptop 12GB
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: 59.7 tok/s decode · 3.2s TTFT (warm) · 149 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	59.7 tok/s	1770 ms	32K
Coding	S	Runs well	59.7 tok/s	3246 ms	32K
Agentic Coding	S	Runs with offload	57.4 tok/s	4902 ms	32K
Reasoning	S	Runs well	59.7 tok/s	3836 ms	32K
RAG	S	Runs with offload	59.7 tok/s	5901 ms	32K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S92
Q3_K_S	3	4.4 GB	Low	S93
NVFP4	4	5.0 GB	Medium	S94
Q4_K_M	4	5.5 GB	Medium	S94
Q5_K_M	5	6.5 GB	High	S94
Q6_KBest for your GPU	6	7.4 GB	High	S93
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

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 4000 Ada Laptop 12GB run Qwen 3.5 9B?

Yes, RTX 4000 Ada Laptop 12GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 59.7 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 9.8 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 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Qwen 3.5 9B achieves approximately 59.7 tokens per second decode speed with a time-to-first-token of 3246ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RTX 4000 Ada Laptop 12GB receives a S grade with 59.7 tok/s and 32K context.

What context window can Qwen 3.5 9B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Qwen 3.5 9B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Qwen 3.5 9B

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