Can Qwen 2.5 Coder 1.5B run on NVIDIA L40S 48GB?

YES — Runs Great

B60Good

○Estimated from fit model

Qwen 2.5 Coder 1.5B needs ~7.0 GB VRAM. NVIDIA L40S 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 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) — 7.0 GB, 24.0 tok/s, Runs well

7.0 GB required48.0 GB available

15% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

33K

Memory

7.0 GB / 48.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 1.5B 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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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	24.0 tok/s	4400 ms	33K
Coding	B	Runs well	24.0 tok/s	8067 ms	33K
Agentic Coding	B	Runs well	24.0 tok/s	11733 ms	33K
Reasoning	B	Runs well	24.0 tok/s	9533 ms	33K
RAG	B	Runs well	24.0 tok/s	14667 ms	33K