Can Qwen 2.5 Coder 14B run on NVIDIA A16 64GB?

YES — Runs Great

B61Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~19.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 19.1 GB, 59.2 tok/s, Runs well

19.1 GB required64.0 GB available

30% VRAM used

Fit status

Runs well

Decode

59.2 tok/s

TTFT

3271 ms

Safe context

131K

Memory

19.1 GB / 64.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on NVIDIA A16 64GB
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: 59.2 tok/s decode · 3.3s TTFT (warm) · 148 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	59.2 tok/s	1784 ms	131K
Coding	B	Runs well	59.2 tok/s	3271 ms	131K
Agentic Coding	B	Runs well	59.2 tok/s	4758 ms	131K
Reasoning	B	Runs well	59.2 tok/s	3866 ms	131K
RAG	B	Runs well	59.2 tok/s	5947 ms	131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C55
Q3_K_S	3	6.9 GB	Low	B55
NVFP4	4	7.8 GB	Medium	B55
Q4_K_M	4	8.5 GB	Medium	B55
Q5_K_M	5	10.1 GB	High	B56
Q6_K	6	11.5 GB	High	B56
Q8_0	8	15.0 GB	Very High	B56
F16Best for your GPU	16	28.7 GB	Maximum	B60

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

NVIDIA A100 80GBOpción económica

80 GB VRAM (+16)2039 GB/s (+1439)

Sube la velocidad estimada de decodificación alrededor de un 231%.196 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 231%.

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

~$15,000 MSRP

NVIDIA A800 80GBMejor relación calidad-precio

80 GB VRAM (+16)1935 GB/s (+1335)

Sube la velocidad estimada de decodificación alrededor de un 222%.190.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 222%.

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

~$15,000 MSRP

NVIDIA H800 80GBMejora NVIDIA

80 GB VRAM (+16)3000 GB/s (+2400)

Sube la velocidad estimada de decodificación alrededor de un 231%.196 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 231%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Qwen 2.5 Coder 14B?

Yes, NVIDIA A16 64GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 59.2 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 19.1 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 A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Coder 14B achieves approximately 59.2 tokens per second decode speed with a time-to-first-token of 3271ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on NVIDIA A16 64GB receives a B grade with 59.2 tok/s and 131K context.

What context window can Qwen 2.5 Coder 14B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Coder 14B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Qwen 2.5 Coder 14B

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