Can Qwen 2.5 Coder 7B run on RTX A4000 16GB?

YES — Runs Great

A72Great

○Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.9 GB VRAM. RTX A4000 16GB has 16.0 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) — 7.9 GB, 79.7 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

79.7 tok/s

TTFT

2428 ms

Safe context

131K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on RTX A4000 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: 79.7 tok/s decode · 2.4s TTFT (warm) · 199 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	79.7 tok/s	1324 ms	131K
Coding	A	Runs well	79.7 tok/s	2428 ms	131K
Agentic Coding	A	Runs well	79.7 tok/s	3532 ms	131K
Reasoning	A	Runs well	79.7 tok/s	2869 ms	131K
RAG	A	Runs well	79.7 tok/s	4414 ms	131K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B68
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0Best for your GPU	8	7.5 GB	Very High	A71
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 A4000 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	61.4 tok/s
👁 Alibaba Qwen 3 14B	14B	S	39.7 tok/s
👁 Alibaba Qwen 3 8B	8B	S	69.1 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	37.6 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	35 tok/s

Frequently asked questions

See all results for RTX A4000 16GB See all hardware for Qwen 2.5 Coder 7B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-7b-on-a4000-16gb

⇱ Qwen 2.5 Coder 7B on RTX A4000 16GB? YES