Can Qwen 2.5 Coder 1.5B run on RTX 5060 8GB?

YES — Runs Great

B65Good

○Estimated from fit model

Qwen 2.5 Coder 1.5B needs ~3.0 GB VRAM. RTX 5060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 3.0 GB, 28.5 tok/s, Runs well

3.0 GB required8.0 GB available

38% VRAM used

Fit status

Runs well

Decode

28.5 tok/s

TTFT

6793 ms

Safe context

33K

Memory

3.0 GB / 8.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 1.5B on RTX 5060 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: 28.5 tok/s decode · 6.8s TTFT (warm) · 71 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	28.5 tok/s	3705 ms	33K
Coding	B	Runs well	28.5 tok/s	6793 ms	33K
Agentic Coding	B	Runs well	28.5 tok/s	9881 ms	33K
Reasoning	B	Runs well	28.5 tok/s	8028 ms	33K
RAG	B	Runs well	28.5 tok/s	12351 ms	33K

Quantization options

How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on RTX 5060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B68
Q3_K_S	3	0.7 GB	Low	B68
NVFP4	4	0.8 GB	Medium	B68
Q4_K_M	4	0.9 GB	Medium	B69
Q5_K_M	5	1.1 GB	High	B69
Q6_K	6	1.2 GB	High	B69
Q8_0	8	1.6 GB	Very High	B70
F16Best for your GPU	16	3.1 GB	Maximum	A73

Get started

Copy-paste commands to run Qwen 2.5 Coder 1.5B on your machine.

Run

ollama run qwen2.5-coder:1.5b

Frequently asked questions

See all results for RTX 5060 8GB See all hardware for Qwen 2.5 Coder 1.5B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-1.5b-on-rtx-5060-8gb