Can Qwen 2.5 14B run on NVIDIA A2 16GB?

YES — Tight Fit

A80Great

Estimated from fit model

Qwen 2.5 14B needs ~14.3 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: OllamaCapacity: TightBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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, 19.7 tok/s, Tight fit

14.3 GB required16.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

19.7 tok/s

TTFT

9813 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 NVIDIA A2 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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 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	19.7 tok/s	5353 ms	25K
Coding	A	Tight fit	19.7 tok/s	9813 ms	25K
Agentic Coding	B	Runs with offload (needs ~0.6 GB host RAM)	12.7 tok/s	22160 ms	25K
Reasoning	A	Tight fit	19.7 tok/s	11598 ms	25K
RAG	B	Runs with offload (needs ~0.6 GB host RAM)	12.7 tok/s	27700 ms	25K

Quantization options

How Qwen 2.5 14B (14B params) fits at each quantization level on NVIDIA A2 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	7.8 GB	Medium	A82
Q4_K_M	4	8.5 GB	Medium	A82
Q5_K_M	5	10.1 GB	High	A82
Q6_KBest for your GPU	6	11.5 GB	High	A82
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

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 A2 16GB can run

Model	Params	Grade	Decode
Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s
GPT-OSS 20B	21B	A	17.4 tok/s
Codestral 2 25.08	22B	B	6.8 tok/s
CogVLM2 19B	19B	A	9.8 tok/s

Frequently asked questions

Can NVIDIA A2 16GB run Qwen 2.5 14B?

Yes, NVIDIA A2 16GB can run Qwen 2.5 14B with a A grade (Tight fit). Expected decode speed: 19.7 tok/s.

How much VRAM does Qwen 2.5 14B need?

Qwen 2.5 14B (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 14B?

The recommended quantization for Qwen 2.5 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 14B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Qwen 2.5 14B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9813ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Qwen 2.5 14B for coding?

For coding workloads, Qwen 2.5 14B on NVIDIA A2 16GB receives a A grade with 19.7 tok/s and 25K context.

What context window can Qwen 2.5 14B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Qwen 2.5 14B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for Qwen 2.5 14B

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<iframe src="https://willitrunai.com/embed/qwen-2.5-14b-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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