Can Qwen 2.5 Coder 14B run on NVIDIA A100 40GB?

YES — Runs Great

B65Good

○Estimated from fit model

Qwen 2.5 Coder 14B needs ~16.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~153 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) — 16.7 GB, 165.2 tok/s, Runs well

16.7 GB required40.0 GB available

42% VRAM used

Fit status

Runs well

Decode

165.2 tok/s

TTFT

1172 ms

Safe context

131K

Memory

16.7 GB / 40.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on NVIDIA A100 40GB
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: 165.2 tok/s decode · 1.2s TTFT (warm) · 413 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	165.2 tok/s	639 ms	131K
Coding	B	Runs well	153.0 tok/s	1266 ms	131K
Agentic Coding	B	Runs well	165.2 tok/s	1705 ms	131K
Reasoning	B	Runs well	165.2 tok/s	1385 ms	131K
RAG	B	Runs well	165.2 tok/s	2131 ms	131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B57
Q3_K_S	3	6.9 GB	Low	B57
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 A100 40GB See all hardware for Qwen 2.5 Coder 14B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-14b-on-a100-40gb

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