Can Qwen 2.5 Coder 14B run on NVIDIA A2 16GB?

YES — Tight Fit

B63Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.3 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 14.3 GB, 19.7 tok/s, Tight fit

14.3 GB required16.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

19.7 tok/s

TTFT

9813 ms

Safe context

25K

Memory

14.3 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on NVIDIA A2 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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 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	19.7 tok/s	5353 ms	25K
Coding	B	Tight fit	19.7 tok/s	9813 ms	25K
Agentic Coding	C	Runs with offload (needs ~0.6 GB host RAM)	12.7 tok/s	22160 ms	25K
Reasoning	B	Tight fit	19.7 tok/s	11598 ms	25K
RAG	C	Runs with offload (needs ~0.6 GB host RAM)	12.7 tok/s	27700 ms	25K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA A2 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

Opções de upgrade

Hardware que roda bem Qwen 2.5 Coder 14B

RTX 4000 Ada 20GBOpção econômica

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.35.5 tok/s decodificação

Raises estimated decode speed by about 80%.

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

~$1,250 MSRP

RTX 3090 24GBMelhor custo-benefício

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 321%.82.9 tok/s decodificação

Raises estimated decode speed by about 321%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBUpgrade NVIDIA

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 262%.71.4 tok/s decodificação

Raises estimated decode speed by about 262%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Qwen 2.5 Coder 14B?

Yes, NVIDIA A2 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 19.7 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.3 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, Qwen 2.5 Coder 14B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9813ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on NVIDIA A2 16GB receives a B grade with 19.7 tok/s and 25K context.

What context window can Qwen 2.5 Coder 14B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Qwen 2.5 Coder 14B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB 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-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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