Can Qwen 2.5 Coder 14B run on RTX 5000 Ada 32GB?

YES — Runs Great

B65Good

○Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 15.9 GB, 58.3 tok/s, Runs well

15.9 GB required32.0 GB available

50% VRAM used

Fit status

Runs well

Decode

58.3 tok/s

TTFT

3322 ms

Safe context

104K

Memory

15.9 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on RTX 5000 Ada 32GB
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: 58.3 tok/s decode · 3.3s TTFT (warm) · 146 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	58.3 tok/s	1812 ms	104K
Coding	B	Runs well	54.0 tok/s	3588 ms	104K
Agentic Coding	B	Runs well	58.3 tok/s	4832 ms	104K
Reasoning	B	Runs well	58.3 tok/s	3926 ms	104K
RAG	B	Runs well	58.3 tok/s	6040 ms	104K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B58
Q3_K_S	3	6.9 GB	Low	B59
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 5000 Ada 32GB See all hardware for Qwen 2.5 Coder 14B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-14b-on-rtx-5000-ada-32gb

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