Can Qwen 2.5 Coder 14B run on RTX 3090 24GB?

YES — Runs Great

B69Good

○Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.1 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~77 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 15.1 GB, 82.9 tok/s, Runs well

15.1 GB required24.0 GB available

63% VRAM used

Fit status

Runs well

Decode

82.9 tok/s

TTFT

2336 ms

Safe context

65K

Memory

15.1 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on RTX 3090 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: 82.9 tok/s decode · 2.3s TTFT (warm) · 207 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	B	Runs well	82.9 tok/s	1274 ms	65K
Coding	B	Runs well	76.7 tok/s	2523 ms	65K
Agentic Coding	A	Runs well	82.9 tok/s	3399 ms	65K
Reasoning	B	Runs well	82.9 tok/s	2761 ms	65K
RAG	A	Runs well	82.9 tok/s	4248 ms	65K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Frequently asked questions

See all results for RTX 3090 24GB See all hardware for Qwen 2.5 Coder 14B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-14b-on-rtx-3090-24gb

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