Can Qwen 2.5 Coder 14B run on RX 9070 16GB?

YES — Tight Fit

B66Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~50 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, 50.2 tok/s, Tight fit

14.0 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

50.2 tok/s

TTFT

3858 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 RX 9070 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: 50.2 tok/s decode · 3.9s TTFT (warm) · 126 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	50.2 tok/s	2104 ms	27K
Coding	B	Tight fit	50.2 tok/s	3858 ms	27K
Agentic Coding	C	Runs with offload (needs ~0.5 GB host RAM)	34.3 tok/s	8204 ms	27K
Reasoning	B	Tight fit	50.2 tok/s	4560 ms	27K
RAG	C	Runs with offload (needs ~0.5 GB host RAM)	34.3 tok/s	10256 ms	27K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RX 9070 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	7.8 GB	Medium	B66
Q4_K_M	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	B65
Q6_KBest for your GPU	6	11.5 GB	High	B65
Q8_0	8	15.0 GB	Very High	F0
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

Opciones de mejora

Hardware que ejecuta bien Qwen 2.5 Coder 14B

RX 7900 XT 20GBOpción económica

20 GB VRAM (+4)800 GB/s (+160)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.60.7 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RX 7900 XTX 24GBMejor relación calidad-precio

24 GB VRAM (+8)960 GB/s (+320)

Sube la velocidad estimada de decodificación alrededor de un 74%.87.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 74%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$999 MSRP

AMD Instinct MI100 32GBMejora AMD

32 GB VRAM (+16)1228 GB/s (+588)

Sube la velocidad estimada de decodificación alrededor de un 101%.100.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 101%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$11,500 MSRP

Frequently asked questions

Can RX 9070 16GB run Qwen 2.5 Coder 14B?

Yes, RX 9070 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 50.2 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 RX 9070 16GB?

On RX 9070 16GB, Qwen 2.5 Coder 14B achieves approximately 50.2 tokens per second decode speed with a time-to-first-token of 3858ms using Q4_K_M quantization.

Can RX 9070 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RX 9070 16GB receives a B grade with 50.2 tok/s and 27K context.

What context window can Qwen 2.5 Coder 14B use on RX 9070 16GB?

On RX 9070 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 RX 9070 16GB See all hardware for Qwen 2.5 Coder 14B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-rx-9070-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: