Can Qwen 3.5 9B run on NVIDIA A10 24GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Qwen 3.5 9B needs ~11.3 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~92 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) — 11.3 GB, 91.6 tok/s, Runs well

11.3 GB required24.0 GB available

47% VRAM used

Fit status

Runs well

Decode

91.6 tok/s

TTFT

2113 ms

Safe context

109K

Memory

11.3 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on NVIDIA A10 24GB
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: 91.6 tok/s decode · 2.1s TTFT (warm) · 229 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	91.6 tok/s	1152 ms	109K
Coding	S	Runs well	91.6 tok/s	2113 ms	109K
Agentic Coding	S	Runs well	91.6 tok/s	3073 ms	109K
Reasoning	S	Runs well	91.6 tok/s	2497 ms	109K
RAG	S	Runs well	91.6 tok/s	3841 ms	109K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S86
Q3_K_S	3	4.4 GB	Low	S87
NVFP4	4	5.0 GB	Medium	S87
Q4_K_M	4	5.5 GB	Medium	S87
Q5_K_M	5	6.5 GB	High	S88
Q6_K	6	7.4 GB	High	S88
Q8_0	8	9.6 GB	Very High	S90
F16Best for your GPU	16	18.5 GB	Maximum	S91

Get started

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

Run

ollama run qwen3.5:9b

Your hardware

More models your NVIDIA A10 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.5 27B	27B	S	30.7 tok/s
Qwen 3.6 27B	27B	S	30.8 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s

Frequently asked questions

Can NVIDIA A10 24GB run Qwen 3.5 9B?

Yes, NVIDIA A10 24GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 91.6 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 11.3 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, Qwen 3.5 9B achieves approximately 91.6 tokens per second decode speed with a time-to-first-token of 2113ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on NVIDIA A10 24GB receives a S grade with 91.6 tok/s and 109K context.

What context window can Qwen 3.5 9B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Qwen 3.5 9B can safely use up to 109K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A10 24GB See all hardware for Qwen 3.5 9B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.5-9b-on-a10-24gb" 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

91.6 tok/s

1152 ms

109K

Coding

Runs well

91.6 tok/s

2113 ms

109K

Agentic Coding

Runs well

91.6 tok/s

3073 ms

109K

Reasoning

Runs well

91.6 tok/s

2497 ms

109K

RAG

Runs well

91.6 tok/s

3841 ms

109K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

S86

Q3_K_S

4.4 GB

Low

S87

NVFP4

5.0 GB

Medium

S87

Q4_K_M

5.5 GB

Medium

S87

Q5_K_M

6.5 GB

High

S88

Q6_K

7.4 GB

High

S88

Q8_0

9.6 GB

Very High

S90

F16Best for your GPU

18.5 GB

Maximum

S91

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

70.8 tok/s

Qwen 3.5 27B

27B

30.7 tok/s

Qwen 3.6 27B

27B

30.8 tok/s

Qwen3-VL 30B A3B Instruct

30B

73.2 tok/s

Frequently asked questions

Can NVIDIA A10 24GB run Qwen 3.5 9B?

Yes, NVIDIA A10 24GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 91.6 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 11.3 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, Qwen 3.5 9B achieves approximately 91.6 tokens per second decode speed with a time-to-first-token of 2113ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on NVIDIA A10 24GB receives a S grade with 91.6 tok/s and 109K context.

What context window can Qwen 3.5 9B use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, Qwen 3.5 9B can safely use up to 109K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.