Can Qwen3-Coder 30B A3B Instruct run on Radeon AI PRO R9700 32GB?

YES — Runs Great

S98Excellent

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~24.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 24.2 GB, 57.1 tok/s, Runs well

24.2 GB required32.0 GB available

76% VRAM used

Fit status

Runs well

Decode

57.1 tok/s

TTFT

3391 ms

Safe context

102K

Memory

24.2 GB / 32.0 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on Radeon AI PRO R9700 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: 57.1 tok/s decode · 3.4s TTFT (warm) · 143 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	57.1 tok/s	1849 ms	102K
Coding	S	Runs well	52.5 tok/s	3687 ms	102K
Agentic Coding	S	Runs well	57.1 tok/s	4932 ms	102K
Reasoning	S	Runs well	57.1 tok/s	4007 ms	102K
RAG	S	Runs well	57.1 tok/s	6165 ms	102K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on Radeon AI PRO R9700 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

Get started

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

Run

ollama run qwen3-coder

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Qwen3-Coder 30B A3B Instruct?

Yes, Radeon AI PRO R9700 32GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 52.5 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 24.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen3-Coder 30B A3B Instruct achieves approximately 52.5 tokens per second decode speed with a time-to-first-token of 3687ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on Radeon AI PRO R9700 32GB receives a S grade with 52.5 tok/s and 102K context.

What context window can Qwen3-Coder 30B A3B Instruct use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen3-Coder 30B A3B Instruct can safely use up to 102K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Qwen3-Coder 30B A3B Instruct

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