Can Qwen 2.5 Coder 3B run on RTX 5050 8GB?

YES — Runs Great

A81Great

○Estimated from fit model

Qwen 2.5 Coder 3B needs ~5.7 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 5.7 GB, 57.0 tok/s, Runs well

5.7 GB required8.0 GB available

71% VRAM used

Fit status

Runs well

Decode

57.0 tok/s

TTFT

3396 ms

Safe context

33K

Memory

5.7 GB / 8.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B on RTX 5050 8GB
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: 57.0 tok/s decode · 3.4s TTFT (warm) · 143 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	57.0 tok/s	1853 ms	33K
Coding	A	Runs well	57.0 tok/s	3396 ms	33K
Agentic Coding	A	Runs with offload	57.0 tok/s	4940 ms	33K
Reasoning	A	Runs well	57.0 tok/s	4014 ms	33K
RAG	A	Runs with offload	57.0 tok/s	6175 ms	33K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on RTX 5050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A76
Q3_K_S	3	1.5 GB	Low	A76
NVFP4	4	1.7 GB	Medium	A77
Q4_K_M	4	1.8 GB	Medium	A77
Q5_K_M	5	2.2 GB	High	A78
Q6_K	6	2.5 GB	High	A78
Q8_0Best for your GPU	8	3.2 GB	Very High	A79
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 3B on your machine.

Run

ollama run qwen2.5-coder:3b

Your hardware

More models your RTX 5050 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	A	20.3 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	76 tok/s
👁 Alibaba Qwen 3 8B	8B	A	26.2 tok/s
👁 Microsoft Phi-4 Mini Reasoning 4B	3.8B	S	72.2 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	27.8 tok/s

Frequently asked questions

See all results for RTX 5050 8GB See all hardware for Qwen 2.5 Coder 3B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-3b-on-rtx-5050-8gb