Can Qwen 2.5 Coder 14B run on Radeon PRO W7700 16GB?

YES — Tight Fit

B66Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.0 GB, 43.0 tok/s, Tight fit

14.0 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

43.0 tok/s

TTFT

4505 ms

Safe context

27K

Memory

14.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on Radeon PRO W7700 16GB
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: 43.0 tok/s decode · 4.5s TTFT (warm) · 107 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	43.0 tok/s	2457 ms	27K
Coding	B	Tight fit	43.0 tok/s	4505 ms	27K
Agentic Coding	C	Runs with offload (needs ~0.5 GB host RAM)	28.7 tok/s	9802 ms	27K
Reasoning	B	Tight fit	43.0 tok/s	5324 ms	27K
RAG	C	Runs with offload (needs ~0.5 GB host RAM)	28.7 tok/s	12253 ms

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B64
Q3_K_S	3	6.9 GB	Low	B65
NVFP4	4

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

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Raises estimated decode speed by about 41%.60.7 tok/s decode

Raises estimated decode speed by about 41%.

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

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Raises estimated decode speed by about 103%.87.4 tok/s decode

Raises estimated decode speed by about 103%.

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

~$999 MSRP

AMD Instinct MI100 32GBAMD upgrade

32 GB VRAM (+16)1228 GB/s (+652)

Raises estimated decode speed by about 135%.100.9 tok/s decode

Raises estimated decode speed by about 135%.

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

~$11,500 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run Qwen 2.5 Coder 14B?

Yes, Radeon PRO W7700 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 43.0 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.0 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 PRO W7700 16GB?

On Radeon PRO W7700 16GB, Qwen 2.5 Coder 14B achieves approximately 43.0 tokens per second decode speed with a time-to-first-token of 4505ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Radeon PRO W7700 16GB receives a B grade with 43.0 tok/s and 27K context.

What context window can Qwen 2.5 Coder 14B use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Qwen 2.5 Coder 14B can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GB See all hardware for Qwen 2.5 Coder 14B

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