Can Qwen 2.5 Coder 3B run on NVIDIA T4 16GB?

YES — Runs Great

A74Great

○Estimated from fit model

Qwen 2.5 Coder 3B needs ~6.8 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~42 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) — 6.8 GB, 42.0 tok/s, Runs well

6.8 GB required16.0 GB available

43% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

83K

Memory

6.8 GB / 16.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B on NVIDIA T4 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: 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	83K
Coding	A	Runs well	42.0 tok/s	4610 ms	83K
Agentic Coding	A	Runs well	42.0 tok/s	6705 ms	83K
Reasoning	A	Runs well	42.0 tok/s	5448 ms	83K
RAG	A	Runs well	42.0 tok/s	8381 ms	83K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A71
Q3_K_S	3	1.5 GB	Low	A71
NVFP4	4	1.7 GB	Medium	A72
Q4_K_M	4	1.8 GB	Medium	A72
Q5_K_M	5	2.2 GB	High	A72
Q6_K	6	2.5 GB	High	A72
Q8_0	8	3.2 GB	Very High	A73
F16Best for your GPU	16	6.1 GB	Maximum	A76

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 NVIDIA T4 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	40.7 tok/s
👁 Alibaba Qwen 3 14B	14B	S	26.3 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	56 tok/s
👁 Alibaba Qwen 3 8B	8B	S	45.8 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	24.9 tok/s

Frequently asked questions

See all results for NVIDIA T4 16GB See all hardware for Qwen 2.5 Coder 3B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-3b-on-t4-16gb

⇱ Qwen 2.5 Coder 3B on NVIDIA T4 16GB? YES