Can Qwen 2.5 Coder 3B run on RTX 2080 Ti 11GB?

YES — Runs Great

A77Great

○Estimated from fit model

Qwen 2.5 Coder 3B needs ~6.3 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 6.3 GB, 42.0 tok/s, Runs well

6.3 GB required11.0 GB available

57% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

50K

Memory

6.3 GB / 11.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B on RTX 2080 Ti 11GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	42.0 tok/s	2514 ms	50K
Coding	A	Runs well	42.0 tok/s	4610 ms	50K
Agentic Coding	A	Runs well	42.0 tok/s	6705 ms	50K
Reasoning	A	Runs well	42.0 tok/s	5448 ms	50K
RAG	A	Runs well	42.0 tok/s	8381 ms	50K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A73
Q3_K_S	3	1.5 GB	Low	A74
NVFP4	4	1.7 GB	Medium	A74
Q4_K_M	4	1.8 GB	Medium	A74
Q5_K_M	5	2.2 GB	High	A75
Q6_K	6	2.5 GB	High	A75
Q8_0	8	3.2 GB	Very High	A76
F16Best for your GPU	16	6.1 GB	Maximum	A78

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 2080 Ti 11GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	78.4 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	56 tok/s
👁 Alibaba Qwen 3 8B	8B	S	88.2 tok/s
👁 Microsoft Phi-4 Mini Reasoning 4B	3.8B	S	53.2 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	S	88.2 tok/s

Frequently asked questions

See all results for RTX 2080 Ti 11GB See all hardware for Qwen 2.5 Coder 3B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-3b-on-rtx-2080-ti-11gb

⇱ Qwen 2.5 Coder 3B on RTX 2080 Ti 11GB? YES