Can Qwen 2.5 14B run on NVIDIA A16 64GB?

YES — Runs Great

A78Great

○Estimated from fit model

Qwen 2.5 14B needs ~19.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~59 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) — 19.1 GB, 59.2 tok/s, Runs well

19.1 GB required64.0 GB available

30% VRAM used

Fit status

Runs well

Decode

59.2 tok/s

TTFT

3271 ms

Safe context

131K

Memory

19.1 GB / 64.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 14B on NVIDIA A16 64GB
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: 59.2 tok/s decode · 3.3s TTFT (warm) · 148 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	59.2 tok/s	1784 ms	131K
Coding	A	Runs well	59.2 tok/s	3271 ms	131K
Agentic Coding	A	Runs well	59.2 tok/s	4758 ms	131K
Reasoning	A	Runs well	59.2 tok/s	3866 ms	131K
RAG	A	Runs well	59.2 tok/s	5947 ms	131K

Quantization options

How Qwen 2.5 14B (14B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A71
Q3_K_S	3	6.9 GB	Low	A72
NVFP4	4	7.8 GB	Medium	A72
Q4_K_M	4	8.5 GB	Medium	A72
Q5_K_M	5	10.1 GB	High	A72
Q6_K	6	11.5 GB	High	A72
Q8_0	8	15.0 GB	Very High	A73
F16Best for your GPU	16	28.7 GB	Maximum	A76

Get started

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

Run

ollama run qwen2.5

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.7 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	30.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	59.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Qwen 2.5 14B

URL: https://willitrunai.com/can-run/qwen-2.5-14b-on-a16-64gb