Can Qwen 2.5 Coder 14B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

B65Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~48 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) — 15.6 GB, 47.8 tok/s, Runs well

15.6 GB required32.0 GB available

49% VRAM used

Fit status

Runs well

Decode

47.8 tok/s

TTFT

4054 ms

Safe context

106K

Memory

15.6 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B 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: 47.8 tok/s decode · 4.1s TTFT (warm) · 119 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	B	Runs well	47.8 tok/s	2211 ms	106K
Coding	B	Runs well	47.8 tok/s	4054 ms	106K
Agentic Coding	B	Runs well	47.8 tok/s	5897 ms	106K
Reasoning	B	Runs well	47.8 tok/s	4791 ms	106K
RAG	B	Runs well	47.8 tok/s	7371 ms	106K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B58
Q3_K_S	3	6.9 GB	Low	B59
NVFP4	4	7.8 GB	Medium	B59
Q4_K_M	4	8.5 GB	Medium	B59
Q5_K_M	5	10.1 GB	High	B60
Q6_K	6	11.5 GB	High	B61
Q8_0Best for your GPU	8	15.0 GB	Very High	B63
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

NVIDIA A100 40GBBudget pick

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 246%.165.2 tok/s decode

Raises estimated decode speed by about 246%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Qwen 2.5 Coder 14B?

Yes, Radeon AI PRO R9700 32GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 2.5 Coder 14B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4054ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Radeon AI PRO R9700 32GB receives a B grade with 47.8 tok/s and 106K context.

What context window can Qwen 2.5 Coder 14B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 2.5 Coder 14B can safely use up to 106K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Qwen 2.5 Coder 14B

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

47.8 tok/s

2211 ms

106K

Coding

Runs well

47.8 tok/s

4054 ms

106K

Agentic Coding

Runs well

47.8 tok/s

5897 ms

106K

Reasoning

Runs well

47.8 tok/s

4791 ms

106K

RAG

Runs well

47.8 tok/s

7371 ms

106K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B58
Q3_K_S	3	6.9 GB	Low	B59
NVFP4	4	7.8 GB	Medium	B59
Q4_K_M	4	8.5 GB	Medium	B59
Q5_K_M	5	10.1 GB	High	B60
Q6_K	6	11.5 GB	High	B61
Q8_0Best for your GPU	8	15.0 GB	Very High	B63
F16	16	28.7 GB	Maximum	F0

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Qwen 2.5 Coder 14B?

Yes, Radeon AI PRO R9700 32GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 2.5 Coder 14B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4054ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Radeon AI PRO R9700 32GB receives a B grade with 47.8 tok/s and 106K context.

What context window can Qwen 2.5 Coder 14B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen 2.5 Coder 14B can safely use up to 106K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.