Can Qwen 2.5 Coder 14B run on NVIDIA L40S 48GB?

YES — Runs Great

B63Good

○Estimated from fit model

Qwen 2.5 Coder 14B needs ~17.2 GB VRAM. NVIDIA L40S 48GB has 48.0 GB. With Q4_K_M quantization, expect ~79 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 17.2 GB, 89.5 tok/s, Runs well

17.2 GB required48.0 GB available

36% VRAM used

Fit status

Runs well

Decode

89.5 tok/s

TTFT

2163 ms

Safe context

131K

Memory

17.2 GB / 48.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on NVIDIA L40S 48GB
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: 89.5 tok/s decode · 2.2s TTFT (warm) · 224 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	89.5 tok/s	1180 ms	131K
Coding	B	Runs well	78.9 tok/s	2453 ms	131K
Agentic Coding	B	Runs well	89.5 tok/s	3147 ms	131K
Reasoning	B	Runs well	89.5 tok/s	2557 ms	131K
RAG	B	Runs well	89.5 tok/s	3933 ms	131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA L40S 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B56
Q3_K_S	3	6.9 GB	Low	B56
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 NVIDIA L40S 48GB See all hardware for Qwen 2.5 Coder 14B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-14b-on-l40s-48gb

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