Can Qwen 2.5 Coder 32B run on NVIDIA A100 40GB?

YES — Runs Great

A84Great

○Estimated from fit model

Qwen 2.5 Coder 32B needs ~28.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~72 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) — 28.6 GB, 72.3 tok/s, Runs well

28.6 GB required40.0 GB available

72% VRAM used

Fit status

Runs well

Decode

72.3 tok/s

TTFT

2679 ms

Safe context

63K

Memory

28.6 GB / 40.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B 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: 72.3 tok/s decode · 2.7s TTFT (warm) · 181 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	72.3 tok/s	1461 ms	63K
Coding	A	Runs well	72.3 tok/s	2679 ms	63K
Agentic Coding	A	Runs well	72.3 tok/s	3897 ms	63K
Reasoning	A	Runs well	72.3 tok/s	3166 ms	63K
RAG	A	Runs well	72.3 tok/s	4871 ms	63K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A73
Q3_K_S	3	15.7 GB	Low	A74
NVFP4	4	17.9 GB	Medium	A75
Q4_K_M	4	19.5 GB	Medium	A76
Q5_K_M	5	23.0 GB	High	A76
Q6_KBest for your GPU	6	26.2 GB	High	A76
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5-coder

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.6 35B A3B	35B	S	166 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	180.5 tok/s
👁 Moonshot AI Kimi Linear 48B A3B	48B	A	44.6 tok/s

Frequently asked questions

See all results for NVIDIA A100 40GB See all hardware for Qwen 2.5 Coder 32B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-32b-on-a100-40gb

⇱ Qwen 2.5 Coder 32B on NVIDIA A100 40GB? YES