Can Qwen 2.5 Coder 14B run on RTX 5000 Ada Laptop 16GB?

YES — Tight Fit

B67Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 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, 55.8 tok/s, Tight fit

14.0 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

55.8 tok/s

TTFT

3467 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 RTX 5000 Ada Laptop 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: 55.8 tok/s decode · 3.5s TTFT (warm) · 140 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	55.8 tok/s	1891 ms	27K
Coding	B	Tight fit	55.8 tok/s	3467 ms	27K
Agentic Coding	B	Runs with offload (needs ~0.5 GB host RAM)	37.3 tok/s	7545 ms	27K
Reasoning	B	Tight fit	55.8 tok/s	4098 ms	27K
RAG	B	Runs with offload (needs ~0.5 GB host RAM)	37.3 tok/s	9431 ms

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 5000 Ada Laptop 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

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.35.5 tok/s decode

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+360)

Raises estimated decode speed by about 46%.81.2 tok/s decode

Raises estimated decode speed by about 46%.

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

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Raises estimated decode speed by about 82%.101.7 tok/s decode

Raises estimated decode speed by about 82%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run Qwen 2.5 Coder 14B?

Yes, RTX 5000 Ada Laptop 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 55.8 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 RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Qwen 2.5 Coder 14B achieves approximately 55.8 tokens per second decode speed with a time-to-first-token of 3467ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RTX 5000 Ada Laptop 16GB receives a B grade with 55.8 tok/s and 27K context.

What context window can Qwen 2.5 Coder 14B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 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 RTX 5000 Ada Laptop 16GB See all hardware for Qwen 2.5 Coder 14B

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