Can Qwen 2.5 Coder 7B run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

A75Great

○Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.5 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~80 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) — 7.5 GB, 80.2 tok/s, Runs well

7.5 GB required12.0 GB available

63% VRAM used

Fit status

Runs well

Decode

80.2 tok/s

TTFT

2414 ms

Safe context

100K

Memory

7.5 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on RTX 4000 Ada Laptop 12GB
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: 80.2 tok/s decode · 2.4s TTFT (warm) · 201 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	A	Runs well	80.2 tok/s	1317 ms	100K
Coding	A	Runs well	80.2 tok/s	2414 ms	100K
Agentic Coding	A	Runs well	80.2 tok/s	3512 ms	100K
Reasoning	A	Runs well	80.2 tok/s	2853 ms	100K
RAG	A	Runs well	80.2 tok/s	4390 ms	100K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B69
Q3_K_S	3	3.4 GB	Low	B70
NVFP4	4	3.9 GB	Medium	A70
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A72
Q6_K	6	5.7 GB	High	A72
Q8_0Best for your GPU	8	7.5 GB	Very High	A72
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 7B on your machine.

Run

ollama run qwen2.5-coder:7b

Your hardware

More models your RTX 4000 Ada Laptop 12GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	61.8 tok/s
👁 Alibaba Qwen 3 14B	14B	A	23.8 tok/s
👁 Alibaba Qwen 3 8B	8B	S	69.5 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	S	69.5 tok/s
👁 Mistral Ministral 3 14B	14B	A	23.7 tok/s

Frequently asked questions

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Qwen 2.5 Coder 7B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-7b-on-rtx-4000-ada-laptop-12gb

⇱ Qwen 2.5 Coder 7B on RTX 4000 Ada Laptop 12GB? YES