Can Qwen 2.5 Coder 14B run on NVIDIA L4 24GB?

YES — Runs Great

B65Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 15.1 GB, 24.7 tok/s, Runs well

15.1 GB required24.0 GB available

63% VRAM used

Fit status

Runs well

Decode

24.7 tok/s

TTFT

7851 ms

Safe context

65K

Memory

15.1 GB / 24.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on NVIDIA L4 24GB
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: 24.7 tok/s decode · 7.9s TTFT (warm) · 62 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	24.7 tok/s	4282 ms	65K
Coding	B	Runs well	24.7 tok/s	7851 ms	65K
Agentic Coding	B	Runs well	24.7 tok/s	11419 ms	65K
Reasoning	B	Runs well	24.7 tok/s	9278 ms	65K
RAG	B	Runs well	24.7 tok/s	14274 ms	65K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B61
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
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

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 515%.151.8 tok/s decode

Raises estimated decode speed by about 515%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 285%.95.2 tok/s decode

Raises estimated decode speed by about 285%.

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

~$2,499 MSRP

NVIDIA V100 32GBNVIDIA upgrade

32 GB VRAM (+8)900 GB/s (+600)

Raises estimated decode speed by about 209%.76.3 tok/s decode

Raises estimated decode speed by about 209%.

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

~$8,999 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Qwen 2.5 Coder 14B?

Yes, NVIDIA L4 24GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 24.7 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.1 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 L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 Coder 14B achieves approximately 24.7 tokens per second decode speed with a time-to-first-token of 7851ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on NVIDIA L4 24GB receives a B grade with 24.7 tok/s and 65K context.

What context window can Qwen 2.5 Coder 14B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 Coder 14B can safely use up to 65K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB 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-l4-24gb" 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

24.7 tok/s

4282 ms

65K

Coding

Runs well

24.7 tok/s

7851 ms

65K

Agentic Coding

Runs well

24.7 tok/s

11419 ms

65K

Reasoning

Runs well

24.7 tok/s

9278 ms

65K

RAG

Runs well

24.7 tok/s

14274 ms

65K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B61
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
F16	16	28.7 GB	Maximum	F0

Frequently asked questions

Can NVIDIA L4 24GB run Qwen 2.5 Coder 14B?

Yes, NVIDIA L4 24GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 24.7 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.1 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 L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 Coder 14B achieves approximately 24.7 tokens per second decode speed with a time-to-first-token of 7851ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on NVIDIA L4 24GB receives a B grade with 24.7 tok/s and 65K context.

What context window can Qwen 2.5 Coder 14B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 Coder 14B can safely use up to 65K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.