Can Qwen 2.5 14B run on RTX 2000 Ada 16GB?

YES — Tight Fit

A81Great

○Estimated from fit model

Qwen 2.5 14B needs ~14.3 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 14.3 GB, 27.7 tok/s, Tight fit

14.3 GB required16.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

27.7 tok/s

TTFT

6994 ms

Safe context

25K

Memory

14.3 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 14B on RTX 2000 Ada 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 27.7 tok/s decode · 7.0s TTFT (warm) · 69 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	27.7 tok/s	3815 ms	25K
Coding	A	Tight fit	27.7 tok/s	6994 ms	25K
Agentic Coding	B	Runs with offload (needs ~0.6 GB host RAM)	17.8 tok/s	15794 ms	25K
Reasoning	A	Tight fit	27.7 tok/s	8266 ms	25K
RAG	B	Runs with offload (needs ~0.6 GB host RAM)	17.8 tok/s	19742 ms

Quantization options

How Qwen 2.5 14B (14B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A80
Q3_K_S	3	6.9 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 14B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your RTX 2000 Ada 16GB can run

Model	Params	Grade	Decode	Capabilities
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	26.2 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	24.4 tok/s

Frequently asked questions

See all results for RTX 2000 Ada 16GB See all hardware for Qwen 2.5 14B

URL: https://willitrunai.com/can-run/qwen-2.5-14b-on-rtx-2000-ada-16gb

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