Can Qwen 2.5 14B run on NVIDIA A100 40GB?

YES — Runs Great

A82Great

Estimated from fit model

Qwen 2.5 14B needs ~16.7 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~165 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) — 16.7 GB, 165.2 tok/s, Runs well

16.7 GB required40.0 GB available

42% VRAM used

Fit status

Runs well

Decode

165.2 tok/s

TTFT

1172 ms

Safe context

131K

Memory

16.7 GB / 40.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 14B on NVIDIA A100 40GB
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: 165.2 tok/s decode · 1.2s TTFT (warm) · 413 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	165.2 tok/s	639 ms	131K
Coding	A	Runs well	165.2 tok/s	1172 ms	131K
Agentic Coding	A	Runs well	165.2 tok/s	1705 ms	131K
Reasoning	A	Runs well	165.2 tok/s	1385 ms	131K
RAG	A	Runs well	165.2 tok/s	2131 ms	131K

Inference speed

Qwen 2.5 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.9	Fits

Quantization options

How Qwen 2.5 14B (14B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A73
Q3_K_S	3	6.9 GB	Low	A74
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 A100 40GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.5 27B	27B	S	85.7 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Qwen 2.5 14B?

Yes, NVIDIA A100 40GB can run Qwen 2.5 14B with a A grade (Runs well). Expected decode speed: 165.2 tok/s.

How much VRAM does Qwen 2.5 14B need?

Qwen 2.5 14B (14B parameters) requires approximately 16.7 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 A100 40GB?

On NVIDIA A100 40GB, Qwen 2.5 14B achieves approximately 165.2 tokens per second decode speed with a time-to-first-token of 1172ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Qwen 2.5 14B for coding?

For coding workloads, Qwen 2.5 14B on NVIDIA A100 40GB receives a A grade with 165.2 tok/s and 131K context.

What context window can Qwen 2.5 14B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Qwen 2.5 14B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Qwen 2.5 14B

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