Can Qwen 3.5 9B run on RTX 5000 Ada Laptop 16GB?

YES — Runs Great

S97Excellent

Estimated from fit model

Qwen 3.5 9B needs ~10.5 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~82 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 10.5 GB, 82.3 tok/s, Runs well

10.5 GB required16.0 GB available

66% VRAM used

Fit status

Runs well

Decode

82.3 tok/s

TTFT

2351 ms

Safe context

56K

Memory

10.5 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on RTX 5000 Ada Laptop 16GB
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: 82.3 tok/s decode · 2.4s TTFT (warm) · 206 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	82.3 tok/s	1283 ms	56K
Coding	S	Runs well	82.3 tok/s	2351 ms	56K
Agentic Coding	S	Runs well	82.3 tok/s	3420 ms	56K
Reasoning	S	Runs well	82.3 tok/s	2779 ms	56K
RAG	S	Runs well	82.3 tok/s	4275 ms	56K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S89
Q3_K_S	3	4.4 GB	Low	S90
NVFP4	4	5.0 GB	Medium	S90
Q4_K_M	4	5.5 GB	Medium	S91
Q5_K_M	5	6.5 GB	High	S92
Q6_K	6	7.4 GB	High	S93
Q8_0Best for your GPU	8	9.6 GB	Very High	S93
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 5000 Ada Laptop 16GB run Qwen 3.5 9B?

Yes, RTX 5000 Ada Laptop 16GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 82.3 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 10.5 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 RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Qwen 3.5 9B achieves approximately 82.3 tokens per second decode speed with a time-to-first-token of 2351ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RTX 5000 Ada Laptop 16GB receives a S grade with 82.3 tok/s and 56K context.

What context window can Qwen 3.5 9B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Qwen 3.5 9B can safely use up to 56K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for Qwen 3.5 9B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3.5-9b-on-rtx-5000-ada-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

82.3 tok/s

1283 ms

56K

Coding

Runs well

82.3 tok/s

2351 ms

56K

Agentic Coding

Runs well

82.3 tok/s

3420 ms

56K

Reasoning

Runs well

82.3 tok/s

2779 ms

56K

RAG

Runs well

82.3 tok/s

4275 ms

56K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S89
Q3_K_S	3	4.4 GB	Low	S90
NVFP4	4	5.0 GB	Medium	S90
Q4_K_M	4	5.5 GB	Medium	S91
Q5_K_M	5	6.5 GB	High	S92
Q6_K	6	7.4 GB	High	S93
Q8_0Best for your GPU	8	9.6 GB	Very High	S93
F16	16	18.5 GB	Maximum	F0

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run Qwen 3.5 9B?

Yes, RTX 5000 Ada Laptop 16GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 82.3 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 10.5 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 RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Qwen 3.5 9B achieves approximately 82.3 tokens per second decode speed with a time-to-first-token of 2351ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on RTX 5000 Ada Laptop 16GB receives a S grade with 82.3 tok/s and 56K context.

What context window can Qwen 3.5 9B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Qwen 3.5 9B can safely use up to 56K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.