Can Qwen 2.5 Coder 0.5B run on NVIDIA L40 48GB?

YES — Runs Great

C45Usable

○Estimated from fit model

Qwen 2.5 Coder 0.5B needs ~6.2 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Memory bandwidth

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) — 6.2 GB, 8.0 tok/s, Runs well

6.2 GB required48.0 GB available

13% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24200 ms

Safe context

131K

Memory

6.2 GB / 48.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 0.5B on NVIDIA L40 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: 8.0 tok/s decode · 24.2s TTFT (warm) · 20 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 7.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	8.0 tok/s	13200 ms	131K
Coding	C	Runs well	7.0 tok/s	27657 ms	131K
Agentic Coding	C	Runs well	8.0 tok/s	35200 ms	131K
Reasoning	C	Runs well	8.0 tok/s	28600 ms	131K
RAG	C	Runs well	8.0 tok/s	44000 ms	131K