Can Qwen 2.5 32B run on RTX PRO 4500 Blackwell 32GB?

YES — Tight Fit

A85Great

○Estimated from fit model

Qwen 2.5 32B needs ~27.8 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 27.8 GB, 41.6 tok/s, Tight fit

27.8 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

41.6 tok/s

TTFT

4649 ms

Safe context

33K

Memory

27.8 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B on RTX PRO 4500 Blackwell 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: 41.6 tok/s decode · 4.6s TTFT (warm) · 104 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	41.6 tok/s	2536 ms	33K
Coding	A	Tight fit	41.6 tok/s	4649 ms	33K
Agentic Coding	A	Runs with offload	41.6 tok/s	6762 ms	33K
Reasoning	A	Tight fit	41.6 tok/s	5494 ms	33K
RAG	A	Runs with offload	41.6 tok/s	8453 ms	33K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on RTX PRO 4500 Blackwell 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A81
Q3_K_S	3	15.7 GB	Low	A83
NVFP4	4	17.9 GB	Medium	A83
Q4_K_M	4	19.5 GB	Medium	A83
Q5_K_MBest for your GPU	5	23.0 GB	High	A82
Q6_K	6	26.2 GB	High	F0
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 RTX PRO 4500 Blackwell 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	95.6 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	104 tok/s

Frequently asked questions

See all results for RTX PRO 4500 Blackwell 32GB See all hardware for Qwen 2.5 32B

URL: https://willitrunai.com/can-run/qwen-2.5-32b-on-rtx-pro-4500-blackwell-32gb

⇱ Qwen 2.5 32B on RTX PRO 4500 Blackwell 32GB? TIGHT FIT