Can Qwen 2.5 14B run on NVIDIA V100 32GB?

YES — Runs Great

A83Great

Estimated from fit model

Qwen 2.5 14B needs ~15.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 15.9 GB, 76.3 tok/s, Runs well

15.9 GB required32.0 GB available

50% VRAM used

Fit status

Runs well

Decode

76.3 tok/s

TTFT

2539 ms

Safe context

104K

Memory

15.9 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 14B on NVIDIA V100 32GB
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: 76.3 tok/s decode · 2.5s TTFT (warm) · 191 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	76.3 tok/s	1385 ms	104K
Coding	A	Runs well	76.3 tok/s	2539 ms	104K
Agentic Coding	A	Runs well	76.3 tok/s	3693 ms	104K
Reasoning	A	Runs well	76.3 tok/s	3000 ms	104K
RAG	A	Runs well	76.3 tok/s	4616 ms	104K

Quantization options

How Qwen 2.5 14B (14B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A75
Q3_K_S	3	6.9 GB	Low	A75
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 NVIDIA V100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Qwen 2.5 14B?

Yes, NVIDIA V100 32GB can run Qwen 2.5 14B with a A grade (Runs well). Expected decode speed: 76.3 tok/s.

How much VRAM does Qwen 2.5 14B need?

Qwen 2.5 14B (14B parameters) requires approximately 15.9 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 V100 32GB?

On NVIDIA V100 32GB, Qwen 2.5 14B achieves approximately 76.3 tokens per second decode speed with a time-to-first-token of 2539ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Qwen 2.5 14B for coding?

For coding workloads, Qwen 2.5 14B on NVIDIA V100 32GB receives a A grade with 76.3 tok/s and 104K context.

What context window can Qwen 2.5 14B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Qwen 2.5 14B can safely use up to 104K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Qwen 2.5 14B

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