Can Qwen 2.5 32B run on NVIDIA A40 48GB?

YES — Runs Great

A85Great

○Estimated from fit model

Qwen 2.5 32B needs ~29.4 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~30 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) — 29.4 GB, 30.0 tok/s, Runs well

29.4 GB required48.0 GB available

61% VRAM used

Fit status

Runs well

Decode

30.0 tok/s

TTFT

6446 ms

Safe context

92K

Memory

29.4 GB / 48.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B on NVIDIA A40 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: 30.0 tok/s decode · 6.4s TTFT (warm) · 75 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	A	Runs well	30.0 tok/s	3516 ms	92K
Coding	A	Runs well	30.0 tok/s	6446 ms	92K
Agentic Coding	S	Runs well	30.0 tok/s	9375 ms	92K
Reasoning	A	Runs well	30.0 tok/s	7617 ms	92K
RAG	S	Runs well	30.0 tok/s	11719 ms	92K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A77
Q3_K_S	3	15.7 GB	Low	A78
NVFP4	4	17.9 GB	Medium	A79
Q4_K_M	4	19.5 GB	Medium	A80
Q5_K_M	5	23.0 GB	High	A81
Q6_K	6	26.2 GB	High	A82
Q8_0Best for your GPU	8	34.2 GB	Very High	A81
F16	16	65.6 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 32B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your NVIDIA A40 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.6 35B A3B	35B	S	69 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	75 tok/s
👁 Alibaba Qwen 2.5 VL 72B	72B	A	7.6 tok/s
👁 Alibaba Qwen3-Coder-Next	80B	A	19.7 tok/s
👁 Meta Llama 3.3 70B	70B	A	8.2 tok/s

Frequently asked questions

See all results for NVIDIA A40 48GB See all hardware for Qwen 2.5 32B

URL: https://willitrunai.com/can-run/qwen-2.5-32b-on-a40-48gb