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

YES — Runs Great

S89Excellent

Estimated from fit model

Qwen 2.5 32B needs ~28.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~67 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 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	S	Runs well	72.3 tok/s	1461 ms	63K
Coding	S	Runs well	66.9 tok/s	2893 ms	63K
Agentic Coding	S	Runs well	72.3 tok/s	3897 ms	63K
Reasoning	S	Runs well	72.3 tok/s	3166 ms	63K
RAG	S	Runs well	72.3 tok/s	4871 ms	63K

Quantization options

How Qwen 2.5 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	A79
Q3_K_S	3	15.7 GB	Low	A80
NVFP4	4	17.9 GB	Medium	A81
Q4_K_M	4	19.5 GB	Medium	A82
Q5_K_M	5	23.0 GB	High	A82
Q6_KBest for your GPU	6	26.2 GB	High	A82
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 32B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen 3.5 35B A3B	35B	S	180.5 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Qwen 2.5 32B?

Yes, NVIDIA A100 40GB can run Qwen 2.5 32B with a S grade (Runs well). Expected decode speed: 66.9 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 28.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization for Qwen 2.5 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 32B run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Qwen 2.5 32B achieves approximately 66.9 tokens per second decode speed with a time-to-first-token of 2893ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on NVIDIA A100 40GB receives a S grade with 66.9 tok/s and 63K context.

What context window can Qwen 2.5 32B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Qwen 2.5 32B can safely use up to 63K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

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

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