Can Qwen 3.5 9B run on NVIDIA L4 24GB?

YES — Runs Great

S91Excellent

○Estimated from fit model

Qwen 3.5 9B needs ~11.3 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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, 38.2 tok/s, Runs well

11.3 GB required24.0 GB available

47% VRAM used

Fit status

Runs well

Decode

38.2 tok/s

TTFT

5070 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 L4 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: 38.2 tok/s decode · 5.1s TTFT (warm) · 96 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	38.2 tok/s	2766 ms	109K
Coding	S	Runs well	38.2 tok/s	5070 ms	109K
Agentic Coding	S	Runs well	38.2 tok/s	7375 ms	109K
Reasoning	S	Runs well	38.2 tok/s	5992 ms	109K
RAG	S	Runs well	38.2 tok/s	9219 ms	109K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on NVIDIA L4 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 L4 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	29.5 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	12.8 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	12.8 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	30.5 tok/s

Frequently asked questions

See all results for NVIDIA L4 24GB See all hardware for Qwen 3.5 9B

URL: https://willitrunai.com/can-run/qwen-3.5-9b-on-l4-24gb