Can Qwen3-Coder 30B A3B Instruct run on RTX PRO 4500 Blackwell 32GB?

YES — Runs Great

S100Excellent

○Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~24.5 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~114 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) — 24.5 GB, 113.8 tok/s, Runs well

24.5 GB required32.0 GB available

77% VRAM used

Fit status

Runs well

Decode

113.8 tok/s

TTFT

1701 ms

Safe context

98K

Memory

24.5 GB / 32.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct 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: 113.8 tok/s decode · 1.7s TTFT (warm) · 285 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	113.8 tok/s	928 ms	98K
Coding	S	Runs well	113.8 tok/s	1701 ms	98K
Agentic Coding	S	Runs well	113.8 tok/s	2474 ms	98K
Reasoning	S	Runs well	113.8 tok/s	2010 ms	98K
RAG	S	Runs well	113.8 tok/s	3093 ms	98K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on RTX PRO 4500 Blackwell 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	S90
Q3_K_S	3	14.9 GB	Low	S92
NVFP4	4	17.1 GB	Medium	S93
Q4_K_M	4	18.6 GB	Medium	S92
Q5_K_M	5	22.0 GB	High	S92
Q6_KBest for your GPU	6	25.0 GB	High	S92
Q8_0	8	32.6 GB	Very High	F0
F16	16	62.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Frequently asked questions

See all results for RTX PRO 4500 Blackwell 32GB See all hardware for Qwen3-Coder 30B A3B Instruct

URL: https://willitrunai.com/can-run/qwen-3-coder-30b-a3b-on-rtx-pro-4500-blackwell-32gb

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